Learning to Identify Fuzzy Regions in Magnetic Resonance Images
|
|
- Moris Jennings
- 5 years ago
- Views:
Transcription
1 Learning to Identify Fuzzy Regions in Magnetic Resonance Images Sarah E. Crane and Lawrence O. Hall Department of Computer Science and Engineering, ENB 118 University of South Florida 4202 E. Fowler Ave. Tampa, Fl Abstract This paper presents an approach to automatic heuristic rule generation for tissue labeling in a magnetic resonance (MR) volumetric image of the human brain. The image is clustered with the semi-supervised fuzzy c-means (ssfcm) algorithm. The clusters are then labeled by analyzing the membership of pixels in the cluster and the corresponding ground truth data. Finally, production rules which are capable of labeling unseen data are learned. Production rule cluster type identification error rates decrease as the clusters become more homogeneous. After imposing a minimum of 70% cluster homogeneity on both the training and the testing data sets, this system was tested using 10-fold cross validation on 29 normal slices with an average cluster type identification error rate of 1.2% 1. Introduction With the growing use of magnetic resonance (MR) imaging as a non-invasive diagnostic technique, the need for automatic image segmentation and labeling increases. Thus, a fast and precise automated image region labeling technique is desirable. Automated methods would not only decrease the inherent inconsistencies of segmentations performed by varied human operators but could potentially provide a means to decrease the time before the doctor receives results on tissue growth/shrinkage and can begin to make a diagnostic determination. Numerous experimental methods of image segmentation and labeling have been presented [9, 5, 4, 8, 2, 12, 11], including rule-based systems shown to be successful in labeling normal tissue slices [9] and volumes [3]. Also, partial labeling of abnormal slices/volumes, respectively, was accomplished. These systems rely on hand-generated rules. The goal of the research described here was the automatic generation of rules to label the image data. The data includes the T1, proton density (PD), and T2 weighted images for each slice. The scope of this research was limited to normal MR slices of the human brain. The tissues of interest contained in such slices are white matter, gray matter, and cerebro-spinal fluid (CSF). It is difficult to obtain ground truth for image voxels as it generally requires an expert to outline boundaries on the image. The outlining process is very time consuming and therefore expensive. Hence, we attempt to generate enough labeled data (cluster centroids here) to learn heuristic rules by breaking an image into 10 masks. The masks include an intracranial region, an extracranial region, four quarters of the intracranial region, and four masks produced by including only every fourth voxel of the intracranial region. A semi-supervised clustering algorithm ssfcm [1], is used to generate regions. The region centers are labeled using ground truth data. Each region or cluster gets the label of the class to which the majority of its voxels belong. C4.5 [10], a decision tree builder, takes the 3 feature cluster center location and the class label of each cluster center as input and produces a decision tree. Subsequently, C4.5RULES [10] uses the decision tree and generates a set of production rules. The remainder of this paper includes Section 2 on the use of domain knowledge in the process of learning cluster identification rules, Section 3 describes how our approach is put together and finally a results and discussion section. 2. Domain Knowledge Each of the slices studied in this research are images of the human brain. These images were acquired in the axial plane. Each slice contains images for the three features of interest, T1 weighted, proton density (PD), and T2 weighted images. A spin-echo sequence was used to acquire the T1
2 Figure 1. Example normal raw image (T1, PD, T2). (a) (b) weighted images; otherwise, a fast spin-echo sequence was used. Figure 1 shows an example slice in which each of the feature images appear as intensity images. The scope of this research only covers normal brain images. Slices of both 3mm and 5mm from both GE and Siemens systems were used. The data acquisition sequence can be found in [6]. Since we know that T1, PD, and T2 vary according to the tissue type, the following cluster center initialization was used to help result in more useful final partitions. Given a lack of training data available for all tissue classes, the region between the minimum and the maximum intensities in the image are divided into c-1 regions, where c is the number of clusters. The initial cluster centers are then placed along the boundaries of these regions. Thus, the initial cluster centers are evenly distributed within the available intensity range, if no training data is available. This distribution implements knowledge about the distribution of T1, PD, and T2 in feature space. Any such initialization would also be valid. 3. The segmentation and identification system This system, which allows for the automatic generation of heuristic rules from quantitative data, requires a number of steps: masking, clustering sub-images, cluster labeling, and production rule generation. After the rules are generated they may be applied to clusters of unseen test images. 3.1 Masking In order to generate enough clusters for training given a limited number of available ground truth images, a series of masks is generated for each slice. A mask defines which of an image s voxels are to be considered for further processing. Such voxels are either labeled or unlabeled. Labeled data can serve as a guide to the segmentation stage s label assignment to unclassified data and to the clustering algorithm. From each image, ten masks are produced. One of the extracranial region and another of the intracranial region. The other eight represent two different quarterings of the intracranial region. Four produce quartering by captur- (c) (d) Figure 2. Example Masks (a) intracranial region (b) extracranial region (c) quartered mask (d) interlaced mask. The white region is included. ing only a single quadrant of the image. Another four produce quartering by creating an image of every fourth voxel. If a voxel is not included in a mask, then it is masked out. Row boundaries are ignored and wraparound is used. Each image contains 256 rows and 256 columns. Figure 2 shows examples of the four main types of masks for this slice. Training data for the ssfcm clustering algorithm is generated by randomly selecting a voxel from a cumulative histogram of all the labeled data, of the selected tissue type. This process is repeated until the required number of distinct training voxels (empirically determined to be 750) of each tissue type have been selected. The extracranial or background classes are labeled only as miscellaneous in ground truth. Thus, we are unable to use ground truth to produce training data for bone, skin, muscle, fat, and air. For this reason, it was decided not to produce training data for the extracranial clusters. This decision does not affect the choice of training data for the intracranial clusters since they are segmented separately. 3.2 Clustering The ssfcm algorithm was used as the clustering algorithm. Each of the ten masks of a slice is clustered giving ten sets of cluster center and voxel fuzzy membership data. For the intracranial masks, training data is also utilized during clustering. If training data is provided in the mask, it is used to initialize the cluster centers. Also, the training data is weighted so that the clustering algorithm acts as if there were multiple voxels with the same intensity as the training voxels. We used an empirically determined weight of 200, hence ssfcm treats each training voxels as if it appeared 2
3 (a) (b) Figure 3. a) Clustered Image and b) Ground truth. Dark gray represents gray matter, light gray indicates white matter, white indicates CSF, and black represents, miscellaneous tissue. 200 times. In this manner, the training voxels are weighted more heavily than non-training voxels and the problem of the least squares algorithm preferring equal cluster sizes is combated. The ratio of training voxels to unlabeled voxels varies per intracranial mask and the size of the intracranial region. No training data has been provided for the extracranial mask whose cluster centers are initialized by dividing the minimum to maximum intensity range into the appropriate number of divisions (as determined by the number of classes) and placing the cluster centers at these divisions. The extracranial mask is clustered into three classes. These classes contain air, bone, fat, skin, and muscle. The intracranial masks, however, are clustered into five classes. This represents an over segmentation of the expected three main classes (white matter, gray matter, and CSF). The extra classes take into consideration the fact that both white matter and gray matter sometimes split into multiple clusters. The extra classes are an attempt to compensate for this splitting and keep the classes uncontaminated by other tissues, rather than have multiple classes merged into a single cluster. A clustered image is shown in Figure 3a and the corresponding ground truth image is shown in Figure 3b. 3.3 Cluster labeling After each mask of an image has been clustered, it is necessary to combine the data generated for the extracranial mask with the data generated from each of the intracranial masks. This recombines the intracranial region and the extracranial region; allowing the system to be used to label tissue types in images that were clustered as a single image. The cluster centers of the extracranial region may be combined with the cluster centers of each of the nine segmentations of the intracranial region. This merging yields nine segmentations of sub-images, each segmentation consists of a total of eight clusters: five intracranial cluster centers and three extracranial cluster centers. In all but the full image, regions will exist that were masked out. After the merging of the cluster data, the cluster labels are identified by a combination of the ground truth values of the member voxels and their fuzzy membership values in each cluster. For each voxel x i, membership in class C k, as specified in the ground truth data, is represented by the function k (x i ), as shown in Equation (1). The cluster label matrix, CL, is incremented by the membership value at the location denoted by the voxel s ground truth value, as shown in Equation (2). U ij represents the fuzzy membership matrix, 0 <= j<number of clusters, k (x i ) = 1:x i 2 C k nx 0 otherwise (1) CL jk = ( k (x i ) U ij (2) i=0 FCL j = kjcl jk CL jm ; 8m; k 6= m (3) 0 <= k < number of labels, n = number of voxels, 0 <= i < number of voxels. After this is complete, the class labels for the clusters are determined by simply finding which class has the greatest number of voxels in the cluster, as shown in Equation (3). FCL is the final cluster label. This method is similar to that used by Hillman [8]. Although never seen in our data, should a cluster have no clearly defined class (at least the two maximum values in the fuzzy membership data are equal), then the lower class number is selected. The method described above to determine the cluster type could result in incorrect labels because of the partial volume effect in the voxels. In an effort to minimize these effects, we only use the fuzzy membership grade of the voxel to keep track of the increment to the cluster s size. Thus, if the voxel s maximum fuzzy membership value is 0.7, the cluster label matrix is not incremented by 1.0 to show a complete voxel, but by 0.7. After every cluster in a slice (for a given mask) has been labeled, we normalize the data. During normalization, the range of T1, T2, and PD values are independently scaled into a 0-1 range. With normalization, we hope to overcome much of the inter-image and inter-patient intensity variability. The relationships between T1, PD, and T2 remain relatively constant in each case in the absence of outside influence, such as radiation therapy and chemotherapy, which should not be found in any of our volunteers. The normalized cluster center data along with the cluster label is then ready to be used as training data for C4.5. 3
4 3.4 Production rule generation Since we are using normalized data, training data from different slices and even different volumes may be combined. The paucity of training and testing data presents two difficulties. First, it is difficult to generate a test set large enough to produce an accurate measure of the error rate while keeping a large training set. Second, the error rates of the test set can be highly variable depending on the division of data into training and testing sets. To overcome these difficulties, we use cross-validation which provides a more accurate estimate of the overall error rate [10]. The training data consists of normalized cluster center values (T1, PD, T2) along with the cluster center s tissue type, as assigned in the cluster analysis phase. Figure 4. Cluster Homogeneity vs. % of Total Clusters Used. 4. Results and Discussion Since there is little labeled data available, 10-fold crossvalidation is done. The full labeled data set is broken into 10 partitions each of 90% of the data for training with a unique 10% available for testing. Results will be reported as averages over the 10 folds. The average number of rules from each fold was 20.2 with a standard deviation of 4.4. It was found that misclassification generally occurs when a cluster center is on a border. Table 1 shows the error rates associated with each tissue type from a data set in which there is a minimum required homogeneity for each cluster of 70%. Homogeneity is defined as the percentage of voxels in a cluster which have the same class label as the cluster. For example, in a 70% homogeneous class A cluster, 70% of the member voxels belong to class A. The other 30% of the member voxels may belong to any other class or combination of classes. With no minimum required homogeneity, the error rate is 5% in cluster identification. The difficulty is that a misidentified cluster means all the pixels of the majority of the cluster are incorrectly labeled (a minority of the pixels may be correctly labeled). The other difficulty is that many identified clusters were not very homogeneous as seen in Figure 4. This means that even when the cluster was correctly labeled, the voxel level error could be significant. In our testing, it has been noted that the average cluster center identification error rate decreases as the required cluster homogeneity is increased, see Figure 5. However, this requirement reduces our already small training data set, see Figure 4. A balance was determined to exist, for this data set, at a required homogeneity of 70%. This requires that each cluster in both the training and testing data sets have at least 70% of its member voxels of the cluster label tissue type. For this data set, 81.7% of the total clusters are usable with greater than 50% of the clusters of each tissue type used. Figure 5. Cluster Homogeneity vs. Set Error Testing Additional testing was done using an FCM [7] segmentation. For these tests, the extracranial region was not extracted. For this reason, the number of clusters was also increased. Segmentations were done by individually segmenting 3 slices into 10 and 20 clusters. The resulting cluster centers, after normalization and labeling, were used as a test data set to a production rule set which was trained on all other existing data. The error rates were much higher, 43% and 28% respectively, than were seen when the intracranial region was extracted prior to segmentation [6]. As expected, the error rates fell, to 26% and 21% respectively, when the test data included only clusters with homogeneity 70%. These error rates are much higher than the corresponding ssfcm error rates. This is a result of segmenting the entire image simultaneously rather than extracting the extracranial region and then segmenting. Gray and white matter clusters get confused with skull tissues resulting in the poor segmentation. Clearly, intracranial tissue extraction must be done before clustering for the proposed approach to be effective. This research would benefit greatly from an increased training data set and testing data set. Given a wider variety of training data, the production rules would prove to be a much more accurate predictor of tissue type than is currently seen. It is also clear that clustering with the extracranial portion of the image masked out would need to 4
5 Table 1. Cluster Center Identification Testing Results for Data Set Requiring 70% Homogeneity for both Training and Testing. Tissue Misc. CSF White Gray Avg. Matter Matter Avg. Error Rate (%) Avg. Number of Mislabeled Clusters Avg. Size of NA 243 NA NA 243 Mislabeled Cluster (voxels) Avg. Size of Cluster (voxels) Total # of Voxels NA % of Total Voxels Remaining Voxel Error Introduced by 17:3 Segmentation (%) Voxel Error Added by Rules 80 Voxel Errors Reduced by Rules 24 be done on unseen images to enable reasonable accuracy. A measure of homogeneity of individual clusters would be useful so that those that are not homogeneous can be excluded from labeling and processed further. The results here suggest that with enough training data it will be possible to learn to identify homogeneous clusters and hence label tissues of MR images of the brain. References [1] A. Bensaid, L. Hall, J. Bezdek, and L. Clarke. Partially supervised clustering for image segmentation. Pattern Recognition, 29(5): , [2] J. Bezdek, L. Hall, and L. Clarke. Review of MR image segmentation techniques using pattern recognition. Medical Physics, 20(4): , [3] M. Clark, L. Hall, D. Goldgof, and et al. MRI segmentation using fuzzy clustering techniques: Integrating knowledge. IEEE Engineering in Medicine and Biology, 13(5): , [4] M. Clark, L. Hall, D. Goldgof, R. Velthuizen, M. Murtagh, and M. Silbiger. Automatic tumor segmentation using knowledge-based techniques. IEEE Transactions on Medical Imaging, 17(2): , April [5] M. Clark, L. Hall, C. Li, and D. Goldgof. Knowledge based (re-)clustering. In Proceedings of the 12th IAPR International Conference on Pattern Recognition, pages , Jerusalem, Israel. [6] S. Crane. Automatic generation of heuristic rules to identify MR tissue types. Master s thesis, University of South Florida, Dept. of CSE, Tampa, Fl. [7] L. Hall, A. Bensaid, L. Clarke, and et al. A comparison of neural network and fuzzy clustering techniques in segmenting magnetic resonance images of the brain. IEEE Transactions on Neural Networks, 3(5): , [8] G. Hillman, C. Chang, H. Ying, and et al. Automatic system for brain MRI analysis using a novel combination of fuzzy rule-based and automatic clustering techniques. In Medical Imaging 1995: Image Processing, pages SPIE, February San Diego, CA. [9] C. Li, D. Goldgof, and L. Hall. Automatic segmentation and tissue labeling of MR brain images. IEEE TMI, 12(4): , December [10] J. Quinlan. C4.5: Programs for Machine Learning. Morgan Kaufmann, San Mateo, CA. [11] M. Sonka, S. Tadikonda, and S. Collins. Knowledge-based interpretation of MR brain images. IEEE TMI, 15(4): , August [12] C. Tsai, B. Manjunath, and R. Jagadeesan. Automated segmentation of brain MR images. Pattern Recognition, 28(12): ,
Fast Fuzzy Clustering of Infrared Images. 2. brfcm
Fast Fuzzy Clustering of Infrared Images Steven Eschrich, Jingwei Ke, Lawrence O. Hall and Dmitry B. Goldgof Department of Computer Science and Engineering, ENB 118 University of South Florida 4202 E.
More informationCHAPTER 6 MODIFIED FUZZY TECHNIQUES BASED IMAGE SEGMENTATION
CHAPTER 6 MODIFIED FUZZY TECHNIQUES BASED IMAGE SEGMENTATION 6.1 INTRODUCTION Fuzzy logic based computational techniques are becoming increasingly important in the medical image analysis arena. The significant
More informationMRI Segmentation MIDAS, 2007, 2010
MRI Segmentation MIDAS, 2007, 2010 Lawrence O. Hall, Dmitry Goldgof, Yuhua Gu, Prodip Hore Dept. of Computer Science & Engineering University of South Florida CONTENTS: 1. Introduction... 1 2. Installing
More informationCHAPTER 3 TUMOR DETECTION BASED ON NEURO-FUZZY TECHNIQUE
32 CHAPTER 3 TUMOR DETECTION BASED ON NEURO-FUZZY TECHNIQUE 3.1 INTRODUCTION In this chapter we present the real time implementation of an artificial neural network based on fuzzy segmentation process
More informationKernel Based Fuzzy Ant Clustering with Partition validity
2006 IEEE International Conference on Fuzzy Systems Sheraton Vancouver Wall Centre Hotel, Vancouver, BC, Canada July 6-2, 2006 Kernel Based Fuzzy Ant Clustering with Partition validity Yuhua Gu and Lawrence
More informationComparison Study of Clinical 3D MRI Brain Segmentation Evaluation
Comparison Study of Clinical 3D MRI Brain Segmentation Evaluation Ting Song 1, Elsa D. Angelini 2, Brett D. Mensh 3, Andrew Laine 1 1 Heffner Biomedical Imaging Laboratory Department of Biomedical Engineering,
More informationPartially Supervised Clustering for Image Segmentation
Partially Supervised Clustering for Image Segmentation Amine M. Bensaid, Lawrence O. Hall Department of Computer Science and Engineering University of South Florida Tampa, FL 33620 {bensaid,hall}@csee.usf.edu
More informationSegmentation of Images
Segmentation of Images SEGMENTATION If an image has been preprocessed appropriately to remove noise and artifacts, segmentation is often the key step in interpreting the image. Image segmentation is a
More informationKnowledge-Based Segmentation of Brain MRI Scans Using the Insight Toolkit
Knowledge-Based Segmentation of Brain MRI Scans Using the Insight Toolkit John Melonakos 1, Ramsey Al-Hakim 1, James Fallon 2 and Allen Tannenbaum 1 1 Georgia Institute of Technology, Atlanta GA 30332,
More informationChapter 3 Set Redundancy in Magnetic Resonance Brain Images
16 Chapter 3 Set Redundancy in Magnetic Resonance Brain Images 3.1 MRI (magnetic resonance imaging) MRI is a technique of measuring physical structure within the human anatomy. Our proposed research focuses
More informationPerformance Evaluation of the TINA Medical Image Segmentation Algorithm on Brainweb Simulated Images
Tina Memo No. 2008-003 Internal Memo Performance Evaluation of the TINA Medical Image Segmentation Algorithm on Brainweb Simulated Images P. A. Bromiley Last updated 20 / 12 / 2007 Imaging Science and
More informationFuzzy Ant Clustering by Centroid Positioning
Fuzzy Ant Clustering by Centroid Positioning Parag M. Kanade and Lawrence O. Hall Computer Science & Engineering Dept University of South Florida, Tampa FL 33620 @csee.usf.edu Abstract We
More informationHistograms. h(r k ) = n k. p(r k )= n k /NM. Histogram: number of times intensity level rk appears in the image
Histograms h(r k ) = n k Histogram: number of times intensity level rk appears in the image p(r k )= n k /NM normalized histogram also a probability of occurence 1 Histogram of Image Intensities Create
More informationCAD SYSTEM FOR AUTOMATIC DETECTION OF BRAIN TUMOR THROUGH MRI BRAIN TUMOR DETECTION USING HPACO CHAPTER V BRAIN TUMOR DETECTION USING HPACO
CHAPTER V BRAIN TUMOR DETECTION USING HPACO 145 CHAPTER 5 DETECTION OF BRAIN TUMOR REGION USING HYBRID PARALLEL ANT COLONY OPTIMIZATION (HPACO) WITH FCM (FUZZY C MEANS) 5.1 PREFACE The Segmentation of
More informationSwarm Based Fuzzy Clustering with Partition Validity
Swarm Based Fuzzy Clustering with Partition Validity Lawrence O. Hall and Parag M. Kanade Computer Science & Engineering Dept University of South Florida, Tampa FL 33620 @csee.usf.edu Abstract
More informationAutomatic Generation of Training Data for Brain Tissue Classification from MRI
MICCAI-2002 1 Automatic Generation of Training Data for Brain Tissue Classification from MRI Chris A. Cocosco, Alex P. Zijdenbos, and Alan C. Evans McConnell Brain Imaging Centre, Montreal Neurological
More informationSegmenting Lesions in Multiple Sclerosis Patients James Chen, Jason Su
Segmenting Lesions in Multiple Sclerosis Patients James Chen, Jason Su Radiologists and researchers spend countless hours tediously segmenting white matter lesions to diagnose and study brain diseases.
More informationK-Means Clustering Using Localized Histogram Analysis
K-Means Clustering Using Localized Histogram Analysis Michael Bryson University of South Carolina, Department of Computer Science Columbia, SC brysonm@cse.sc.edu Abstract. The first step required for many
More informationFeature Extraction and Texture Classification in MRI
Extraction and Texture Classification in MRI Jayashri Joshi, Mrs.A.C.Phadke. Marathwada Mitra Mandal s College of Engineering, Pune.. Maharashtra Institute of Technology, Pune. kjayashri@rediffmail.com.
More informationAbstract. 1. Introduction
A New Automated Method for Three- Dimensional Registration of Medical Images* P. Kotsas, M. Strintzis, D.W. Piraino Department of Electrical and Computer Engineering, Aristotelian University, 54006 Thessaloniki,
More informationIEEE TRANSACTIONS ON NEURAL NETWORKS, VOL. 8, NO. 3, MAY A Methodology for Constructing Fuzzy Algorithms for Learning Vector Quantization
IEEE TRANSACTIONS ON NEURAL NETWORKS, VOL. 8, NO. 3, MAY 1997 505 A Methodology for Constructing Fuzzy Algorithms for Learning Vector Quantization Nicolaos B. Karayiannis, Member, IEEE Abstract This paper
More informationISSN: X Impact factor: 4.295
ISSN: 2454-132X Impact factor: 4.295 (Volume3, Issue1) Available online at: www.ijariit.com Performance Analysis of Image Clustering Algorithm Applied to Brain MRI Kalyani R.Mandlik 1, Dr. Suresh S. Salankar
More information8/3/2017. Contour Assessment for Quality Assurance and Data Mining. Objective. Outline. Tom Purdie, PhD, MCCPM
Contour Assessment for Quality Assurance and Data Mining Tom Purdie, PhD, MCCPM Objective Understand the state-of-the-art in contour assessment for quality assurance including data mining-based techniques
More informationBrain Portion Peeling from T2 Axial MRI Head Scans using Clustering and Morphological Operation
159 Brain Portion Peeling from T2 Axial MRI Head Scans using Clustering and Morphological Operation K. Somasundaram Image Processing Lab Dept. of Computer Science and Applications Gandhigram Rural Institute
More informationKnowledge-Based Segmentation of Brain MRI Scans Using the Insight Toolkit
Knowledge-Based Segmentation of Brain MRI Scans Using the Insight Toolkit John Melonakos 1, Ramsey Al-Hakim 1, James Fallon 2 and Allen Tannenbaum 1 1 Georgia Institute of Technology, Atlanta GA 30332,
More informationProstate Detection Using Principal Component Analysis
Prostate Detection Using Principal Component Analysis Aamir Virani (avirani@stanford.edu) CS 229 Machine Learning Stanford University 16 December 2005 Introduction During the past two decades, computed
More informationA Clustering-Based Method for. Brain Tumor Segmentation
Contemporary Engineering Sciences, Vol. 9, 2016, no. 15, 743-754 HIKARI Ltd, www.m-hikari.com http://dx.doi.org/10.12988/ces.2016.6564 A Clustering-Based Method for Brain Tumor Segmentation Idanis Diaz
More informationA Model-Independent, Multi-Image Approach to MR Inhomogeneity Correction
Tina Memo No. 2007-003 Published in Proc. MIUA 2007 A Model-Independent, Multi-Image Approach to MR Inhomogeneity Correction P. A. Bromiley and N.A. Thacker Last updated 13 / 4 / 2007 Imaging Science and
More informationImage Segmentation Based on Watershed and Edge Detection Techniques
0 The International Arab Journal of Information Technology, Vol., No., April 00 Image Segmentation Based on Watershed and Edge Detection Techniques Nassir Salman Computer Science Department, Zarqa Private
More informationA Generic Knowledge-Guided Image Segmentation and Labeling System Using Fuzzy Clustering Algorithms
IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS PART B: CYBERNETICS, VOL. 32, NO. 5, OCTOBER 2002 571 A Generic Knowledge-Guided Image Segmentation and Labeling System Using Fuzzy Clustering Algorithms
More informationNovel Intuitionistic Fuzzy C-Means Clustering for Linearly and Nonlinearly Separable Data
Novel Intuitionistic Fuzzy C-Means Clustering for Linearly and Nonlinearly Separable Data PRABHJOT KAUR DR. A. K. SONI DR. ANJANA GOSAIN Department of IT, MSIT Department of Computers University School
More informationCorrection of Partial Volume Effects in Arterial Spin Labeling MRI
Correction of Partial Volume Effects in Arterial Spin Labeling MRI By: Tracy Ssali Supervisors: Dr. Keith St. Lawrence and Udunna Anazodo Medical Biophysics 3970Z Six Week Project April 13 th 2012 Introduction
More informationCluster Analysis. Ying Shen, SSE, Tongji University
Cluster Analysis Ying Shen, SSE, Tongji University Cluster analysis Cluster analysis groups data objects based only on the attributes in the data. The main objective is that The objects within a group
More informationFuzzy Clustering Algorithms for Effective Medical Image Segmentation
I.J. Intelligent Systems and Applications, 2013, 11, 55-61 Published Online October 2013 in MECS (http://www.mecs-press.org/) DOI: 10.5815/ijisa.2013.11.06 Fuzzy Clustering Algorithms for Effective Medical
More informationChapter 7 UNSUPERVISED LEARNING TECHNIQUES FOR MAMMOGRAM CLASSIFICATION
UNSUPERVISED LEARNING TECHNIQUES FOR MAMMOGRAM CLASSIFICATION Supervised and unsupervised learning are the two prominent machine learning algorithms used in pattern recognition and classification. In this
More informationPerformance Analysis of Clustering Algorithms in Brain Tumor Detection of MR Images
European Journal of Scientific Research ISSN 1450-216X Vol.62 No.3 (2011), pp. 321-330 EuroJournals Publishing, Inc. 2011 http://www.eurojournals.com/ejsr.htm Performance Analysis of Algorithms in Brain
More informationWhole Body MRI Intensity Standardization
Whole Body MRI Intensity Standardization Florian Jäger 1, László Nyúl 1, Bernd Frericks 2, Frank Wacker 2 and Joachim Hornegger 1 1 Institute of Pattern Recognition, University of Erlangen, {jaeger,nyul,hornegger}@informatik.uni-erlangen.de
More informationWebpage: Volume 3, Issue V, May 2015 eissn:
Morphological Image Processing of MRI Brain Tumor Images Using MATLAB Sarla Yadav 1, Parul Yadav 2 and Dinesh K. Atal 3 Department of Biomedical Engineering Deenbandhu Chhotu Ram University of Science
More informationSegmentation and Modeling of the Spinal Cord for Reality-based Surgical Simulator
Segmentation and Modeling of the Spinal Cord for Reality-based Surgical Simulator Li X.C.,, Chui C. K.,, and Ong S. H.,* Dept. of Electrical and Computer Engineering Dept. of Mechanical Engineering, National
More informationCHAPTER 9: Magnetic Susceptibility Effects in High Field MRI
Figure 1. In the brain, the gray matter has substantially more blood vessels and capillaries than white matter. The magnified image on the right displays the rich vasculature in gray matter forming porous,
More informationSupplementary methods
Supplementary methods This section provides additional technical details on the sample, the applied imaging and analysis steps and methods. Structural imaging Trained radiographers placed all participants
More informationNorbert Schuff VA Medical Center and UCSF
Norbert Schuff Medical Center and UCSF Norbert.schuff@ucsf.edu Medical Imaging Informatics N.Schuff Course # 170.03 Slide 1/67 Objective Learn the principle segmentation techniques Understand the role
More informationMIDAS Signal Calibration
Contents MIDAS Signal Calibration A. Maudsley, 2004-2007 Introduction... 1 The Calibration Phantom... 2 Calibration Method... 4 Additional Considerations... 11 Acknowledgements... 12 Introduction The MR
More informationApplication of fuzzy set theory in image analysis. Nataša Sladoje Centre for Image Analysis
Application of fuzzy set theory in image analysis Nataša Sladoje Centre for Image Analysis Our topics for today Crisp vs fuzzy Fuzzy sets and fuzzy membership functions Fuzzy set operators Approximate
More informationSegmentation of MR Images of a Beating Heart
Segmentation of MR Images of a Beating Heart Avinash Ravichandran Abstract Heart Arrhythmia is currently treated using invasive procedures. In order use to non invasive procedures accurate imaging modalities
More informationGlobal Journal of Engineering Science and Research Management
ADVANCED K-MEANS ALGORITHM FOR BRAIN TUMOR DETECTION USING NAIVE BAYES CLASSIFIER Veena Bai K*, Dr. Niharika Kumar * MTech CSE, Department of Computer Science and Engineering, B.N.M. Institute of Technology,
More informationADAPTIVE GRAPH CUTS WITH TISSUE PRIORS FOR BRAIN MRI SEGMENTATION
ADAPTIVE GRAPH CUTS WITH TISSUE PRIORS FOR BRAIN MRI SEGMENTATION Abstract: MIP Project Report Spring 2013 Gaurav Mittal 201232644 This is a detailed report about the course project, which was to implement
More informationmritc: A Package for MRI Tissue Classification
mritc: A Package for MRI Tissue Classification Dai Feng 1 Luke Tierney 2 1 Merck Research Labratories 2 University of Iowa July 2010 Feng & Tierney (Merck & U of Iowa) MRI Tissue Classification July 2010
More informationSampling-Based Ensemble Segmentation against Inter-operator Variability
Sampling-Based Ensemble Segmentation against Inter-operator Variability Jing Huo 1, Kazunori Okada, Whitney Pope 1, Matthew Brown 1 1 Center for Computer vision and Imaging Biomarkers, Department of Radiological
More informationAvailable Online through
Available Online through www.ijptonline.com ISSN: 0975-766X CODEN: IJPTFI Research Article ANALYSIS OF CT LIVER IMAGES FOR TUMOUR DIAGNOSIS BASED ON CLUSTERING TECHNIQUE AND TEXTURE FEATURES M.Krithika
More informationsurface Image reconstruction: 2D Fourier Transform
2/1/217 Chapter 2-3 K-space Intro to k-space sampling (chap 3) Frequenc encoding and Discrete sampling (chap 2) Point Spread Function K-space properties K-space sampling principles (chap 3) Basic Contrast
More informationSKULL STRIPPING OF MRI USING CLUSTERING AND RESONANCE METHOD
International Journal of Knowledge Management & e-learning Volume 3 Number 1 January-June 2011 pp. 19-23 SKULL STRIPPING OF MRI USING CLUSTERING AND RESONANCE METHOD K. Somasundaram 1 & R. Siva Shankar
More informationmaximum likelihood estimates. The performance of
International Journal of Computer Science and Telecommunications [Volume 2, Issue 6, September 2] 8 ISSN 247-3338 An Efficient Approach for Medical Image Segmentation Based on Truncated Skew Gaussian Mixture
More informationIMPLEMENTATION OF FUZZY C MEANS AND SNAKE MODEL FOR BRAIN TUMOR DETECTION
IMPLEMENTATION OF FUZZY C MEANS AND SNAKE MODEL FOR BRAIN TUMOR DETECTION Salwa Shamis Sulaiyam Al-Mazidi, Shrinidhi Shetty, Soumyanath Datta, P. Vijaya Department of Computer Science & Engg., P.O.Box
More informationClassification of Abdominal Tissues by k-means Clustering for 3D Acoustic and Shear-Wave Modeling
1 Classification of Abdominal Tissues by k-means Clustering for 3D Acoustic and Shear-Wave Modeling Kevin T. Looby klooby@stanford.edu I. ABSTRACT Clutter is an effect that degrades the quality of medical
More informationAvailable online Journal of Scientific and Engineering Research, 2019, 6(1): Research Article
Available online www.jsaer.com, 2019, 6(1):193-197 Research Article ISSN: 2394-2630 CODEN(USA): JSERBR An Enhanced Application of Fuzzy C-Mean Algorithm in Image Segmentation Process BAAH Barida 1, ITUMA
More informationMR IMAGE SEGMENTATION
MR IMAGE SEGMENTATION Prepared by : Monil Shah What is Segmentation? Partitioning a region or regions of interest in images such that each region corresponds to one or more anatomic structures Classification
More informationAutomatic segmentation of the cortical grey and white matter in MRI using a Region Growing approach based on anatomical knowledge
Automatic segmentation of the cortical grey and white matter in MRI using a Region Growing approach based on anatomical knowledge Christian Wasserthal 1, Karin Engel 1, Karsten Rink 1 und André Brechmann
More informationRecent Progress on RAIL: Automating Clustering and Comparison of Different Road Classification Techniques on High Resolution Remotely Sensed Imagery
Recent Progress on RAIL: Automating Clustering and Comparison of Different Road Classification Techniques on High Resolution Remotely Sensed Imagery Annie Chen ANNIEC@CSE.UNSW.EDU.AU Gary Donovan GARYD@CSE.UNSW.EDU.AU
More informationInteractive Treatment Planning in Cancer Radiotherapy
Interactive Treatment Planning in Cancer Radiotherapy Mohammad Shakourifar Giulio Trigila Pooyan Shirvani Ghomi Abraham Abebe Sarah Couzens Laura Noreña Wenling Shang June 29, 212 1 Introduction Intensity
More informationAutomatic Generation of Training Data for Brain Tissue Classification from MRI
Automatic Generation of Training Data for Brain Tissue Classification from MRI Chris A. COCOSCO, Alex P. ZIJDENBOS, and Alan C. EVANS http://www.bic.mni.mcgill.ca/users/crisco/ McConnell Brain Imaging
More informationRADIOMICS: potential role in the clinics and challenges
27 giugno 2018 Dipartimento di Fisica Università degli Studi di Milano RADIOMICS: potential role in the clinics and challenges Dr. Francesca Botta Medical Physicist Istituto Europeo di Oncologia (Milano)
More informationIMAGE SEGMENTATION. Václav Hlaváč
IMAGE SEGMENTATION Václav Hlaváč Czech Technical University in Prague Faculty of Electrical Engineering, Department of Cybernetics Center for Machine Perception http://cmp.felk.cvut.cz/ hlavac, hlavac@fel.cvut.cz
More informationAnalysis of Functional MRI Timeseries Data Using Signal Processing Techniques
Analysis of Functional MRI Timeseries Data Using Signal Processing Techniques Sea Chen Department of Biomedical Engineering Advisors: Dr. Charles A. Bouman and Dr. Mark J. Lowe S. Chen Final Exam October
More informationScene-Based Segmentation of Multiple Muscles from MRI in MITK
Scene-Based Segmentation of Multiple Muscles from MRI in MITK Yan Geng 1, Sebastian Ullrich 2, Oliver Grottke 3, Rolf Rossaint 3, Torsten Kuhlen 2, Thomas M. Deserno 1 1 Department of Medical Informatics,
More informationQUANTITATION OF THE PREMATURE INFANT BRAIN VOLUME FROM MR IMAGES USING WATERSHED TRANSFORM AND BAYESIAN SEGMENTATION
QUANTITATION OF THE PREMATURE INFANT BRAIN VOLUME FROM MR IMAGES USING WATERSHED TRANSFORM AND BAYESIAN SEGMENTATION Merisaari Harri 1;2, Teräs Mika 2, Alhoniemi Esa 1, Parkkola Riitta 2;3, Nevalainen
More informationInternational Journal of Engineering Science Invention Research & Development; Vol. I Issue III September e-issn:
Segmentation of MRI Brain image by Fuzzy Symmetry Based Genetic Clustering Technique Ramani R 1,Dr.S.Balaji 2 1 Assistant Professor,,Department of Computer Science,Tumkur University, 2 Director,Jain University,Bangalore
More informationReport of research activities in fuzzy AI and medicine at USF CSE
Report of research activities in fuzzy AI and medicine at USF CSE Horia-Nicolai L. Teodorescu *, Abraham Kandel, Lawrence O. Hall Abstract University of South Florida, Computer Science and Engineering
More informationImproved Classification of Known and Unknown Network Traffic Flows using Semi-Supervised Machine Learning
Improved Classification of Known and Unknown Network Traffic Flows using Semi-Supervised Machine Learning Timothy Glennan, Christopher Leckie, Sarah M. Erfani Department of Computing and Information Systems,
More informationA Generalized Method to Solve Text-Based CAPTCHAs
A Generalized Method to Solve Text-Based CAPTCHAs Jason Ma, Bilal Badaoui, Emile Chamoun December 11, 2009 1 Abstract We present work in progress on the automated solving of text-based CAPTCHAs. Our method
More informationAn Introduction To Automatic Tissue Classification Of Brain MRI. Colm Elliott Mar 2014
An Introduction To Automatic Tissue Classification Of Brain MRI Colm Elliott Mar 2014 Tissue Classification Tissue classification is part of many processing pipelines. We often want to classify each voxel
More informationMEDICAL IMAGE COMPUTING (CAP 5937) LECTURE 9: Medical Image Segmentation (III) (Fuzzy Connected Image Segmentation)
SPRING 2017 1 MEDICAL IMAGE COMPUTING (CAP 5937) LECTURE 9: Medical Image Segmentation (III) (Fuzzy Connected Image Segmentation) Dr. Ulas Bagci HEC 221, Center for Research in Computer Vision (CRCV),
More informationPerformance Evaluation of Basic Segmented Algorithms for Brain Tumor Detection
IOSR Journal of Electronics and Communication Engineering (IOSR-JECE) e-issn: 2278-2834,p- ISSN: 2278-8735. Volume 5, Issue 6 (Mar. - Apr. 203), PP 08-3 Performance Evaluation of Basic Segmented Algorithms
More informationA Study of Medical Image Analysis System
Indian Journal of Science and Technology, Vol 8(25), DOI: 10.17485/ijst/2015/v8i25/80492, October 2015 ISSN (Print) : 0974-6846 ISSN (Online) : 0974-5645 A Study of Medical Image Analysis System Kim Tae-Eun
More informationIDENTIFYING different materials within sampled datasets
74 IEEE TRANSACTIONS ON MEDICAL IMAGING, VOL. 17, NO. 1, FEBRUARY 1998 Partial-Volume Bayesian Classification of Material Mixtures in MR Volume Data Using Voxel Histograms David H. Laidlaw,* Kurt W. Fleischer,
More informationSkull Segmentation of MR images based on texture features for attenuation correction in PET/MR
Skull Segmentation of MR images based on texture features for attenuation correction in PET/MR CHAIBI HASSEN, NOURINE RACHID ITIO Laboratory, Oran University Algeriachaibih@yahoo.fr, nourine@yahoo.com
More informationMedical images, segmentation and analysis
Medical images, segmentation and analysis ImageLab group http://imagelab.ing.unimo.it Università degli Studi di Modena e Reggio Emilia Medical Images Macroscopic Dermoscopic ELM enhance the features of
More informationAN AUTOMATED SEGMENTATION FRAMEWORK FOR BRAIN MRI VOLUMES BASED ON ADAPTIVE MEAN-SHIFT CLUSTERING
AN AUTOMATED SEGMENTATION FRAMEWORK FOR BRAIN MRI VOLUMES BASED ON ADAPTIVE MEAN-SHIFT CLUSTERING Sam Ponnachan 1, Paul Pandi 2,A.Winifred 3,Joselin Jose 4 UG Scholars 1,2,3,4 Department of EIE 1,2 Department
More informationIMAGE SEGMENTATION BY FUZZY C-MEANS CLUSTERING ALGORITHM WITH A NOVEL PENALTY TERM
Computing and Informatics, Vol. 26, 2007, 17 31 IMAGE SEGMENTATION BY FUZZY C-MEANS CLUSTERING ALGORITHM WITH A NOVEL PENALTY TERM Yong Yang School of Information Management Jiangxi University of Finance
More informationClassification. Vladimir Curic. Centre for Image Analysis Swedish University of Agricultural Sciences Uppsala University
Classification Vladimir Curic Centre for Image Analysis Swedish University of Agricultural Sciences Uppsala University Outline An overview on classification Basics of classification How to choose appropriate
More informationFuzzy-Based Extraction of Vascular Structures from Time-of-Flight MR Images
816 Medical Informatics in a United and Healthy Europe K.-P. Adlassnig et al. (Eds.) IOS Press, 2009 2009 European Federation for Medical Informatics. All rights reserved. doi:10.3233/978-1-60750-044-5-816
More informationClustering CS 550: Machine Learning
Clustering CS 550: Machine Learning This slide set mainly uses the slides given in the following links: http://www-users.cs.umn.edu/~kumar/dmbook/ch8.pdf http://www-users.cs.umn.edu/~kumar/dmbook/dmslides/chap8_basic_cluster_analysis.pdf
More informationTime Stamp Detection and Recognition in Video Frames
Time Stamp Detection and Recognition in Video Frames Nongluk Covavisaruch and Chetsada Saengpanit Department of Computer Engineering, Chulalongkorn University, Bangkok 10330, Thailand E-mail: nongluk.c@chula.ac.th
More information3-D MRI Brain Scan Classification Using A Point Series Based Representation
3-D MRI Brain Scan Classification Using A Point Series Based Representation Akadej Udomchaiporn 1, Frans Coenen 1, Marta García-Fiñana 2, and Vanessa Sluming 3 1 Department of Computer Science, University
More informationDEVELOPMENT OF BIOLOGICAL VOXEL-BASED COMPUTATIONAL MODELS FROM DICOM FILES FOR 3-D ELECTROMAGNETIC SIMULATIONS
DEVELOPMENT OF BIOLOGICAL VOXEL-BASED COMPUTATIONAL MODELS FROM DICOM FILES FOR 3-D ELECTROMAGNETIC SIMULATIONS Mustafa Deha Turan Selçuk Çömlekçi e-mail: HTmdturan20002000@yahoo.comTH e-mail: HTscom@mmf.sdu.edu.trTH
More informationChapter 6. The Interpretation Process. (c) 2008 Prof. Dr. Michael M. Richter, Universität Kaiserslautern
Chapter 6 The Interpretation Process The Semantic Function Syntactically an image is simply a matrix of pixels with gray or color values. The intention is that the image mirrors the visual impression of
More informationImproved Version of Kernelized Fuzzy C-Means using Credibility
50 Improved Version of Kernelized Fuzzy C-Means using Credibility Prabhjot Kaur Maharaja Surajmal Institute of Technology (MSIT) New Delhi, 110058, INDIA Abstract - Fuzzy c-means is a clustering algorithm
More information3D Reconstruction of Brain Tumor from 2D MRI s using FCM and Marching cubes
3D Reconstruction of Brain Tumor from 2D MRI s using FCM and Marching cubes Abstract Brain Tumor is an abnormal mass of tissue found in Brain. Some techniques like MRI and CT generate 2D images of internal
More information3D VISUALIZATION OF SEGMENTED CRUCIATE LIGAMENTS 1. INTRODUCTION
JOURNAL OF MEDICAL INFORMATICS & TECHNOLOGIES Vol. 10/006, ISSN 164-6037 Paweł BADURA * cruciate ligament, segmentation, fuzzy connectedness,3d visualization 3D VISUALIZATION OF SEGMENTED CRUCIATE LIGAMENTS
More informationSeeing the Big Picture
Seeing the Big Picture Segmenting Images to Create Data 15.071x The Analytics Edge Image Segmentation Divide up digital images to salient regions/clusters corresponding to individual surfaces, objects,
More information3D VISUALIZATION AND SEGMENTATION OF BRAIN MRI DATA
3D VISUALIZATION AND SEGMENTATION OF BRAIN MRI DATA Konstantin Levinski, Alexei Sourin and Vitali Zagorodnov Nanyang Technological University, Nanyang Avenue, Singapore konstantin.levinski@gmail.com, assourin@ntu.edu.sg,
More informationSemantic Analysis of Medical Images Using Fuzzy Inference Systems
Semantic Analysis of Medical Images Using Fuzzy Inference Systems Norbert Gal 1, Vasile Stoicu-Tivadar 2 Department of Automation and Applied Informatics, Politehnica University of Timisoara, Timisoara,
More informationImproving the detection of excessive activation of ciliaris muscle by clustering thermal images
11 th International Conference on Quantitative InfraRed Thermography Improving the detection of excessive activation of ciliaris muscle by clustering thermal images *University of Debrecen, Faculty of
More informationProcessing math: 100% Intensity Normalization
Intensity Normalization Overall Pipeline 2/21 Intensity normalization Conventional MRI intensites (T1-w, T2-w, PD, FLAIR) are acquired in arbitrary units Images are not comparable across scanners, subjects,
More informationarxiv: v2 [q-bio.qm] 16 Oct 2017
Gulban this is page 1 The relation between color spaces and compositional data analysis demonstrated with magnetic resonance image processing applications O.F. Gulban Maastricht University, Maastricht,
More informationComputer-Aided Detection system for Hemorrhage contained region
Computer-Aided Detection system for Hemorrhage contained region Myat Mon Kyaw Faculty of Information and Communication Technology University of Technology (Yatanarpon Cybercity), Pyin Oo Lwin, Myanmar
More informationUniversity of Florida CISE department Gator Engineering. Clustering Part 2
Clustering Part 2 Dr. Sanjay Ranka Professor Computer and Information Science and Engineering University of Florida, Gainesville Partitional Clustering Original Points A Partitional Clustering Hierarchical
More informationGlobal Thresholding Techniques to Classify Dead Cells in Diffusion Weighted Magnetic Resonant Images
Global Thresholding Techniques to Classify Dead Cells in Diffusion Weighted Magnetic Resonant Images Ravi S 1, A. M. Khan 2 1 Research Student, Department of Electronics, Mangalore University, Karnataka
More informationA Generation Methodology for Numerical Phantoms with Statistically Relevant Variability of Geometric and Physical Properties
A Generation Methodology for Numerical Phantoms with Statistically Relevant Variability of Geometric and Physical Properties Steven Dolly 1, Eric Ehler 1, Yang Lou 2, Mark Anastasio 2, Hua Li 2 (1) University
More informationUnsupervised Learning
Unsupervised Learning Unsupervised learning Until now, we have assumed our training samples are labeled by their category membership. Methods that use labeled samples are said to be supervised. However,
More information