Medical images, segmentation and analysis

Size: px
Start display at page:

Download "Medical images, segmentation and analysis"

Transcription

1 Medical images, segmentation and analysis ImageLab group Università degli Studi di Modena e Reggio Emilia

2 Medical Images Macroscopic Dermoscopic ELM enhance the features of pigmented skin lesion and then the automated clinical diagnosis

3 The lesions Both naevi and melanoma, have neither regular edges and shape, neither uniform color and can be distinguished by the skin based on the fact that for biological reasons the lesion has a darker aspect with respect to the skin. Unfortunately: No bimodal luminance distribution adaptive threshold can fails Most current image acquisition system do not really cope with color calibration Color classification based on absolute threshold fail Revert to unsupervised approaches, worse than trained system but more robust to not calibration acquisitions

4 Contour Definition of lesion contour is not so clear different medical opinions Nevertheless quantitative evaluation provides a mean to assess comparisons We ask dermatologists to draw contours by hand to have a ground truth database Final goal is to provide a boundary extraction of the lesion acceptable for dermatological experts and that can be exploited in further automatic analysis

5 Automated diagnosis The detection of lesion border in a medical image is the first and necessary step for further automatic skin segmentation. In the last years several researches are driven towards automatic skin cancer diagnosis. In ELM the most of them are based on the so-called ABCD-rule of dermatoscopy or on the latter 7-point checklist.

6 Approach We compared four widely employed color clustering algorithms: Median Cut K-Means Fuzzy C-Means Mean Shift Verify performances in identifying lesion versus skin No spatial constraint was employed We verify the influence of the algorithms with respect to the choice of the parameter settings and the lesions characteristics

7 Clustering Study of algorithms and methods for grouping, or classifying objects described either by a set of a measurements or by relationship between them In the case of segmentation, clustering is used to quantize and reduce the number of colors voting the most representatives This is a problem of exclusive and unsupervised classification, the meat of cluster analysis

8 Proposal Color clustering are widely used to find 2 clusters: ideally skin and lesion results not satisfactory due to the high quantization level applied that makes the features less representative We propose to divide the image in a number of clusters k, investigating many different k values, and then use a supervised classification to group the clusters into two super-classes classification based on the recognition of the skin

9 Proposed algorithm Four important steps: Training, Clustering, Classification and Border Extraction Image is quantized by a clustering algorithm, then the levels obtained are portioned in two group called skin and lesion. Right classification optimal boundary recovered Color and spatial classification of the skin obtains a very satisfactory approximation of the ideal cluster portioning.

10 Mean shift Technique for analysis of feature spaces For color image segmentation, the image data is mapped into the feature space (RGB space) Iterative procedure that shifts each data point to the average of data points in the neighborhood The algorithm is based on a kernel K m(x) x is called mean shift The repeated movement of data points to the sample means is called the mean shift algorithm We have implemented the algorithm using the 3D color histogram instead of spatial search window

11 Mean Shift Mean Shift does not need a fixed number of clusters but a radium research neighborhood λ To compare this algo with others we studied the behavior of the algo in function of λ. or the 12 number of clusters Relation between λ and number of clusters λ λ Number of levels To measure distance between data in RGB color space to evaluate the closeness of centroids (clusters), we used the Euclidian distance for each algorithm

12 Skin color training and classification We assume that the lesion occupies mostly the central part of the image and nothing the four angles We use the color of the angles of the image that are not covered by lesion as training color for skin clusters classification.

13 Skin color training and classification A mixed approach was implement to avoid that big lesion that cover some angles produce a worse skin classification Otsu s threshold and some steps of morphological dilatation identify an area that contain securely the lesion

14 Skin classification Double condition was defined. Each angle was investigated separately. For each cluster was extracted the number of pixels belonging to the angle of the image, and the number of pixels present in the image processed. The class is candidate to be classified as skin if are valid both the conditions:

15 Boundary extraction To identify the lesion s region, maximum area and the closeness of barycenter from the center of the image, are considered To extract the boundary from the binary image was used the Chain Code algorithm to run after the edge 1 clustering 2 Training and classification 3 Boundary extraction

16 Experimental results Database: 117 different dermoscopic images acquired with analog device and digitalized with a scanner with a resolution of 768x512 pixel and 24bit color depth Evaluation metrics: Specificity ξ, Sensitivity η and Score ψ (the mean of the firsts two)

17 Clustering accuracy evaluation Clustering algorithm, in the best condition (with a perfect classification of clusters in skin and lesion) could produce a lesion boundary reliable? Clusters are trained with the boundary of the correspondent Ground Truth and the Score ψ is calculated on the lesion area found. We have performed this procedure on the 117 images clustered with the four algorithms and with twelve different number of color levels: 2, 3, 4, 5, 6, 7, 8, 16, 24, 32, 64 and 128.

18 Upper bound performance Four algorithms have a similar behavior with negligible difference and it is evident that increasing the number of clusters, the accuracy of boundary detected increases, for all the algorithms 1 0,98 0,96 ψ 0,94 0,92 0,9 Median Cut K-Means Fuzzy C-Means Mean Shift Number of clusters Median cut tends to have a similar number of pixels for each clusters, then, with few levels and with lesion area very different from skin area, the result fails. With more than two levels the performance becomes similar to the others algorithms. For Mean Shift, when number of levels is low, the function to estimate the λ fails for some images, driving it too high, and producing only one cluster

19 Comparison Number of clusters Media Cut K-Means FP FN xi eta psi FP FN xi eta psi , ,53 0,930 0,940 0, , ,87 0,970 0,940 0, , ,56 0,980 0,920 0, , ,43 0,980 0,880 0, , ,09 0,970 0,920 0, , ,53 0,970 0,870 0, , ,93 0,970 0,910 0, , ,79 0,970 0,910 0, , ,41 0,970 0,950 0, , ,17 0,980 0,950 0, , ,23 0,960 0,920 0, , ,01 0,970 0,910 0, , ,62 0,960 0,930 0, , ,42 0,960 0,910 0, , ,97 0,940 0,930 0, , ,53 0,960 0,930 0, , ,79 0,910 0,930 0, , ,06 0,920 0,940 0, ,8 2503,21 0,810 0,960 0, , ,89 0,890 0,950 0, ,6 1360,83 0,560 0,990 0, ,2 2481,32 0,620 0,970 0, ,92 0,340 1,000 0, ,46 0,380 1,000 0,690 Number of clusters Fuzzy C-Means Mean Shift FP FN xi eta psi FP FN xi eta psi , ,83 0,990 0,910 0, , ,61 0,855 0,914 0, , ,44 0,980 0,880 0, , ,78 0,931 0,899 0, , ,66 0,980 0,930 0, , ,65 0,976 0,922 0, , ,95 0,980 0,920 0, , ,60 0,965 0,926 0, , ,19 0,980 0,930 0, , ,01 0,972 0,931 0, , ,14 0,970 0,890 0, , ,62 0,963 0,952 0, , ,92 0,960 0,930 0, , ,09 0,960 0,959 0, ,5 6989,09 0,940 0,900 0, , ,60 0,963 0,952 0, , ,88 0,880 0,860 0, , ,09 0,960 0,959 0, , ,73 0,800 0,940 0, , ,60 0,958 0,962 0, ,2 2143,94 0,610 0,970 0, , ,09 0,948 0,979 0, ,35 0,250 0,990 0, , ,26 0,920 0,980 0,950

20 Experimental results Using the automatic boundary extraction the results are different because increasing the number of clusters, the sensitivity tends to increase but the specificity decreases, due to the growing of false positive, especially over 24 levels This behavior is 1,00 caused by the 0,90 difficulty to classify ψ 0,80 properly the clusters when this number is 0,70 too high, a part from 0,60 Mean Shift Median Cut K-Means Fuzzy C-Means Mean Shift Number of clusters

21 Mean Shift: the best Mean Shift result stable and decrease negligible its performance only after the 64 levels Median Cut K-Means Fuzzy C-Means Mean Shift Its property to adapt itself to the features of the target image allows it to create clusters that are more distant each other in the feature space, making their classification more straightforward The best boundaries for the algorithms is obtained with 6 clusters for Median Cut, K-Means and Fuzzy C-Means and 128, the higher examined, for Mean Shift.

22 Examples Median Cut K-Means Fuzzy C-Means Mean Shift

23 Discussion After having performed 5616 segmentation runs for the analysis of the upper bound, and 5616 runs with our approach we can assume that color clustering is very suitable for dermatological image segmentation; all color clustering methods if the right number of clusters is used are sufficiently accurate, even if Mean Shift has demonstrated an higher stability w.r.t. the parameter variation.

24 Web Platform To analyze also the qualitative comparison we have asked to human experts We created a Web site with the results of the segmentation, choosing the best configuration for each algorithm, on the whole database and asked the Dermatologists to vote for the skin lesion contours detected of the four algorithms. To enhance the evaluation and to avoid that minds influence the choice, on the image was not indicated the algorithm used.

25 Web Platform

26 Web Platform Also in this case Mean Shift has been evaluates as the best method The importance of this result is implicit because is a double regard that evidence the good performance of this algorithm for both computer and human evaluation.

27 Topological Tree Good segmentation of the lesion is an important step to start the automated diagnosis and have a deep influence on the results. Topological tree description as in [1] is an application where our algorithm could improve the results A starting good segmentation preserve the algorithm, based on a recursive dichotomous Fuzzy C-Means, from errors of skin classification [1] R. Cucchiara, C. Grana, S. Seidenari, G. Pellacani, "Exploiting Color and Topological Features for Region Segmentation with Recursive Fuzzy c-means" in Machine Graphics and Vision, vol. 11, n. 2/3, pp , 2002

28 TT Misclassification Original algorithm [1] Original algorithm [1] started from our segmentation

29 TT Misclassification Original algorithm [1] Original algorithm [1] started from our segmentation

30 Integration with new clustering features Using the segmentation produced by Mean Shift and apply the algorithm for the region analysis, the results could improve due to the better segmentation Original algorithm [1] started from our segmentation Algorithm [1] working on our segmentation

31 Paper These results was submitted and accepted for the conference on "Image Processing," part of the SPIE Medical Imaging Symposium which will be held February 2006 in San Diego, CA

32 Acknowledgment This project has been funded by MIUR- PRIN Project We thanks Dermatologic Department of University of Modena and Reggio Emilia for the data evaluation.

A new interface for manual segmentation of dermoscopic images

A new interface for manual segmentation of dermoscopic images A new interface for manual segmentation of dermoscopic images P.M. Ferreira, T. Mendonça, P. Rocha Faculdade de Engenharia, Faculdade de Ciências, Universidade do Porto, Portugal J. Rozeira Hospital Pedro

More information

An annotation tool for dermoscopic image segmentation

An annotation tool for dermoscopic image segmentation An annotation tool for dermoscopic image segmentation P. M. Ferreira Faculdade de Engenharia, Universidade do Porto Porto, Portugal meb09015@fe.up.pt T. Mendonça Faculdade de Ciências, Universidade do

More information

Short Survey on Static Hand Gesture Recognition

Short Survey on Static Hand Gesture Recognition Short Survey on Static Hand Gesture Recognition Huu-Hung Huynh University of Science and Technology The University of Danang, Vietnam Duc-Hoang Vo University of Science and Technology The University of

More information

Skin Lesion Classification and Segmentation for Imbalanced Classes using Deep Learning

Skin Lesion Classification and Segmentation for Imbalanced Classes using Deep Learning Skin Lesion Classification and Segmentation for Imbalanced Classes using Deep Learning Mohammed K. Amro, Baljit Singh, and Avez Rizvi mamro@sidra.org, bsingh@sidra.org, arizvi@sidra.org Abstract - This

More information

Detection of Melanoma Skin Cancer using Segmentation and Classification Algorithm

Detection of Melanoma Skin Cancer using Segmentation and Classification Algorithm Detection of Melanoma Skin Cancer using Segmentation and Classification Algorithm Mrs. P. Jegadeeshwari Assistant professor/ece CK College of Engineering &Technology Abstract - Melanoma is the most dangerous

More information

CHAPTER 6 MODIFIED FUZZY TECHNIQUES BASED IMAGE SEGMENTATION

CHAPTER 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 information

Time Stamp Detection and Recognition in Video Frames

Time 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 information

Automatic Shadow Removal by Illuminance in HSV Color Space

Automatic Shadow Removal by Illuminance in HSV Color Space Computer Science and Information Technology 3(3): 70-75, 2015 DOI: 10.13189/csit.2015.030303 http://www.hrpub.org Automatic Shadow Removal by Illuminance in HSV Color Space Wenbo Huang 1, KyoungYeon Kim

More information

Available Online through

Available 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 information

The Detection of Faces in Color Images: EE368 Project Report

The Detection of Faces in Color Images: EE368 Project Report The Detection of Faces in Color Images: EE368 Project Report Angela Chau, Ezinne Oji, Jeff Walters Dept. of Electrical Engineering Stanford University Stanford, CA 9435 angichau,ezinne,jwalt@stanford.edu

More information

Automatic nevi segmentation using adaptive mean shift filters and feature analysis

Automatic nevi segmentation using adaptive mean shift filters and feature analysis Automatic nevi segmentation using adaptive mean shift filters and feature analysis Michael A. King a, Tim K. Lee ab*, M. Stella Atkins a, David I. McLean c a Computing Science, Simon Fraser University,

More information

Classification of Subject Motion for Improved Reconstruction of Dynamic Magnetic Resonance Imaging

Classification of Subject Motion for Improved Reconstruction of Dynamic Magnetic Resonance Imaging 1 CS 9 Final Project Classification of Subject Motion for Improved Reconstruction of Dynamic Magnetic Resonance Imaging Feiyu Chen Department of Electrical Engineering ABSTRACT Subject motion is a significant

More information

Vehicle Detection under Day and Night Illumination

Vehicle Detection under Day and Night Illumination Proc. of ISCS-IIA99 Special session on vehicle traffic and surveillance Vehicle Detection under Day and Night Illumination R. Cucchiara, M. Piccardi 2 Dipartimento di Scienze dell Ingegneria Università

More information

Fully Automatic Methodology for Human Action Recognition Incorporating Dynamic Information

Fully Automatic Methodology for Human Action Recognition Incorporating Dynamic Information Fully Automatic Methodology for Human Action Recognition Incorporating Dynamic Information Ana González, Marcos Ortega Hortas, and Manuel G. Penedo University of A Coruña, VARPA group, A Coruña 15071,

More information

COMPUTER AND ROBOT VISION

COMPUTER AND ROBOT VISION VOLUME COMPUTER AND ROBOT VISION Robert M. Haralick University of Washington Linda G. Shapiro University of Washington A^ ADDISON-WESLEY PUBLISHING COMPANY Reading, Massachusetts Menlo Park, California

More information

Tri-modal Human Body Segmentation

Tri-modal Human Body Segmentation Tri-modal Human Body Segmentation Master of Science Thesis Cristina Palmero Cantariño Advisor: Sergio Escalera Guerrero February 6, 2014 Outline 1 Introduction 2 Tri-modal dataset 3 Proposed baseline 4

More information

Estimating the ground truth from multiple individual segmentations with application to skin lesion segmentation

Estimating the ground truth from multiple individual segmentations with application to skin lesion segmentation AUTHORS: X LI et al. 1 Estimating the ground truth from multiple individual segmentations with application to skin lesion segmentation Xiang Li x.li-29@sms.ed.ac.uk Ben Aldridge ben.aldridge.ed.ac.uk Jonathan

More information

Vehicle Detection under Day and Night Illumination

Vehicle Detection under Day and Night Illumination Vehicle Detection under Day and Night Illumination R. Cucchiara 1, M. Piccardi 2 1 Dipartimento di Scienze dell Ingegneria Università di Modena e Reggio Emilia Via Campi 213\b - 41100 Modena, Italy e-mail:

More information

6. Object Identification L AK S H M O U. E D U

6. Object Identification L AK S H M O U. E D U 6. Object Identification L AK S H M AN @ O U. E D U Objects Information extracted from spatial grids often need to be associated with objects not just an individual pixel Group of pixels that form a real-world

More information

COSC160: Detection and Classification. Jeremy Bolton, PhD Assistant Teaching Professor

COSC160: Detection and Classification. Jeremy Bolton, PhD Assistant Teaching Professor COSC160: Detection and Classification Jeremy Bolton, PhD Assistant Teaching Professor Outline I. Problem I. Strategies II. Features for training III. Using spatial information? IV. Reducing dimensionality

More information

Image Analysis Lecture Segmentation. Idar Dyrdal

Image Analysis Lecture Segmentation. Idar Dyrdal Image Analysis Lecture 9.1 - Segmentation Idar Dyrdal Segmentation Image segmentation is the process of partitioning a digital image into multiple parts The goal is to divide the image into meaningful

More information

Improving Positron Emission Tomography Imaging with Machine Learning David Fan-Chung Hsu CS 229 Fall

Improving Positron Emission Tomography Imaging with Machine Learning David Fan-Chung Hsu CS 229 Fall Improving Positron Emission Tomography Imaging with Machine Learning David Fan-Chung Hsu (fcdh@stanford.edu), CS 229 Fall 2014-15 1. Introduction and Motivation High- resolution Positron Emission Tomography

More information

Histogram and watershed based segmentation of color images

Histogram and watershed based segmentation of color images Histogram and watershed based segmentation of color images O. Lezoray H. Cardot LUSAC EA 2607 IUT Saint-Lô, 120 rue de l'exode, 50000 Saint-Lô, FRANCE Abstract A novel method for color image segmentation

More information

How to Detect Moving Shadows: Theory and Practice

How to Detect Moving Shadows: Theory and Practice How to Detect Moving Shadows: Theory and Practice Andrea Prati ImageLab * D.I.I. Università di Modena e Reggio Emilia * http://imagelab.ing.unimo.it Staff: Prof. Rita Cucchiara (head), Grana Costantino,

More information

Lab 9. Julia Janicki. Introduction

Lab 9. Julia Janicki. Introduction Lab 9 Julia Janicki Introduction My goal for this project is to map a general land cover in the area of Alexandria in Egypt using supervised classification, specifically the Maximum Likelihood and Support

More information

MELANOCYTIC GLOBULES DETECTION IN SKIN LESION IMAGES

MELANOCYTIC GLOBULES DETECTION IN SKIN LESION IMAGES MELANOCYTIC GLOBULES DETECTION IN SKIN LESION IMAGES Leszek A. Nowak, Katarzyna Grzesiak-Kopeć, Maciej J. Ogorzałek Department of Information Technologies at Faculty of Physics, Astronomy and Applied Computer

More information

CAP 6412 Advanced Computer Vision

CAP 6412 Advanced Computer Vision CAP 6412 Advanced Computer Vision http://www.cs.ucf.edu/~bgong/cap6412.html Boqing Gong April 21st, 2016 Today Administrivia Free parameters in an approach, model, or algorithm? Egocentric videos by Aisha

More information

Pattern recognition (4)

Pattern recognition (4) Pattern recognition (4) 1 Things we have discussed until now Statistical pattern recognition Building simple classifiers Supervised classification Minimum distance classifier Bayesian classifier (1D and

More information

Texture Segmentation by Windowed Projection

Texture Segmentation by Windowed Projection Texture Segmentation by Windowed Projection 1, 2 Fan-Chen Tseng, 2 Ching-Chi Hsu, 2 Chiou-Shann Fuh 1 Department of Electronic Engineering National I-Lan Institute of Technology e-mail : fctseng@ccmail.ilantech.edu.tw

More information

Markerless human motion capture through visual hull and articulated ICP

Markerless human motion capture through visual hull and articulated ICP Markerless human motion capture through visual hull and articulated ICP Lars Mündermann lmuender@stanford.edu Stefano Corazza Stanford, CA 93405 stefanoc@stanford.edu Thomas. P. Andriacchi Bone and Joint

More information

TRAFFIC surveillance and traffic control systems are

TRAFFIC surveillance and traffic control systems are DRAFT VERSION 1 Improving Shadow Suppression in Moving Object Detection with HSV Color Information Rita Cucchiara, Costantino Grana, Massimo Piccardi, Andrea Prati, Stefano Sirotti Abstract Video-surveillance

More information

Color Local Texture Features Based Face Recognition

Color Local Texture Features Based Face Recognition Color Local Texture Features Based Face Recognition Priyanka V. Bankar Department of Electronics and Communication Engineering SKN Sinhgad College of Engineering, Korti, Pandharpur, Maharashtra, India

More information

Using the Kolmogorov-Smirnov Test for Image Segmentation

Using the Kolmogorov-Smirnov Test for Image Segmentation Using the Kolmogorov-Smirnov Test for Image Segmentation Yong Jae Lee CS395T Computational Statistics Final Project Report May 6th, 2009 I. INTRODUCTION Image segmentation is a fundamental task in computer

More information

Human Body Recognition and Tracking: How the Kinect Works. Kinect RGB-D Camera. What the Kinect Does. How Kinect Works: Overview

Human Body Recognition and Tracking: How the Kinect Works. Kinect RGB-D Camera. What the Kinect Does. How Kinect Works: Overview Human Body Recognition and Tracking: How the Kinect Works Kinect RGB-D Camera Microsoft Kinect (Nov. 2010) Color video camera + laser-projected IR dot pattern + IR camera $120 (April 2012) Kinect 1.5 due

More information

Enhanced Hemisphere Concept for Color Pixel Classification

Enhanced Hemisphere Concept for Color Pixel Classification 2016 International Conference on Multimedia Systems and Signal Processing Enhanced Hemisphere Concept for Color Pixel Classification Van Ng Graduate School of Information Sciences Tohoku University Sendai,

More information

MR IMAGE SEGMENTATION

MR 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 information

Extracting Layers and Recognizing Features for Automatic Map Understanding. Yao-Yi Chiang

Extracting Layers and Recognizing Features for Automatic Map Understanding. Yao-Yi Chiang Extracting Layers and Recognizing Features for Automatic Map Understanding Yao-Yi Chiang 0 Outline Introduction/ Problem Motivation Map Processing Overview Map Decomposition Feature Recognition Discussion

More information

Motion Detection Algorithm

Motion Detection Algorithm Volume 1, No. 12, February 2013 ISSN 2278-1080 The International Journal of Computer Science & Applications (TIJCSA) RESEARCH PAPER Available Online at http://www.journalofcomputerscience.com/ Motion Detection

More information

Introduction to Medical Imaging (5XSA0) Module 5

Introduction to Medical Imaging (5XSA0) Module 5 Introduction to Medical Imaging (5XSA0) Module 5 Segmentation Jungong Han, Dirk Farin, Sveta Zinger ( s.zinger@tue.nl ) 1 Outline Introduction Color Segmentation region-growing region-merging watershed

More information

DATA EMBEDDING IN TEXT FOR A COPIER SYSTEM

DATA EMBEDDING IN TEXT FOR A COPIER SYSTEM DATA EMBEDDING IN TEXT FOR A COPIER SYSTEM Anoop K. Bhattacharjya and Hakan Ancin Epson Palo Alto Laboratory 3145 Porter Drive, Suite 104 Palo Alto, CA 94304 e-mail: {anoop, ancin}@erd.epson.com Abstract

More information

Automatic Diagnosis of Melanoma: a Software System based on the 7-Point Check-List

Automatic Diagnosis of Melanoma: a Software System based on the 7-Point Check-List Automatic Diagnosis of Melanoma: a Software System based on the 7-Point Check-List G. Di Leo, A. Paolillo, P. Sommella D.I.I.I.E. University of Salerno (Italy) {gdileo,apaolillo,psommella}@unisa.it G.

More information

Image segmentation. Stefano Ferrari. Università degli Studi di Milano Methods for Image Processing. academic year

Image segmentation. Stefano Ferrari. Università degli Studi di Milano Methods for Image Processing. academic year Image segmentation Stefano Ferrari Università degli Studi di Milano stefano.ferrari@unimi.it Methods for Image Processing academic year 2017 2018 Segmentation by thresholding Thresholding is the simplest

More information

Comparison Study of Clinical 3D MRI Brain Segmentation Evaluation

Comparison 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 information

Babu Madhav Institute of Information Technology Years Integrated M.Sc.(IT)(Semester - 7)

Babu Madhav Institute of Information Technology Years Integrated M.Sc.(IT)(Semester - 7) 5 Years Integrated M.Sc.(IT)(Semester - 7) 060010707 Digital Image Processing UNIT 1 Introduction to Image Processing Q: 1 Answer in short. 1. What is digital image? 1. Define pixel or picture element?

More information

eyes can easily detect and count these cells, and such knowledge is very hard to duplicate for computer.

eyes can easily detect and count these cells, and such knowledge is very hard to duplicate for computer. Robust Automated Algorithm for Counting Mouse Axions Anh Tran Department of Electrical Engineering Case Western Reserve University, Cleveland, Ohio Email: anh.tran@case.edu Abstract: This paper presents

More information

AUTOMATIC LESION SEGMENTATION FOR MELANOMA DIAGNOSTICS IN MACROSCOPIC IMAGES

AUTOMATIC LESION SEGMENTATION FOR MELANOMA DIAGNOSTICS IN MACROSCOPIC IMAGES AUTOMATIC LESION SEGMENTATION FOR MELANOMA DIAGNOSTICS IN MACROSCOPIC IMAGES Ionuţ Pirnog, Radu Ovidiu Preda, Cristina Oprea, and Constantin Paleologu Department of Telecommunications, University Politehnica

More information

Robust PDF Table Locator

Robust PDF Table Locator Robust PDF Table Locator December 17, 2016 1 Introduction Data scientists rely on an abundance of tabular data stored in easy-to-machine-read formats like.csv files. Unfortunately, most government records

More information

The IEE International Symposium on IMAGING FOR CRIME DETECTION AND PREVENTION, Savoy Place, London, UK 7-8 June 2005

The IEE International Symposium on IMAGING FOR CRIME DETECTION AND PREVENTION, Savoy Place, London, UK 7-8 June 2005 Ambient Intelligence for Security in Public Parks: the LAICA Project Rita Cucchiara, Andrea Prati, Roberto Vezzani Dipartimento di Ingegneria dell Informazione University of Modena and Reggio Emilia, Italy

More information

Digital Image Processing. Prof. P.K. Biswas. Department of Electronics & Electrical Communication Engineering

Digital Image Processing. Prof. P.K. Biswas. Department of Electronics & Electrical Communication Engineering Digital Image Processing Prof. P.K. Biswas Department of Electronics & Electrical Communication Engineering Indian Institute of Technology, Kharagpur Image Segmentation - III Lecture - 31 Hello, welcome

More information

Small-scale objects extraction in digital images

Small-scale objects extraction in digital images 102 Int'l Conf. IP, Comp. Vision, and Pattern Recognition IPCV'15 Small-scale objects extraction in digital images V. Volkov 1,2 S. Bobylev 1 1 Radioengineering Dept., The Bonch-Bruevich State Telecommunications

More information

Norbert Schuff VA Medical Center and UCSF

Norbert 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 information

Computer-Aided Diagnosis in Abdominal and Cardiac Radiology Using Neural Networks

Computer-Aided Diagnosis in Abdominal and Cardiac Radiology Using Neural Networks Computer-Aided Diagnosis in Abdominal and Cardiac Radiology Using Neural Networks Du-Yih Tsai, Masaru Sekiya and Yongbum Lee Department of Radiological Technology, School of Health Sciences, Faculty of

More information

Computer Aided Diagnosis Based on Medical Image Processing and Artificial Intelligence Methods

Computer Aided Diagnosis Based on Medical Image Processing and Artificial Intelligence Methods International Journal of Information and Computation Technology. ISSN 0974-2239 Volume 3, Number 9 (2013), pp. 887-892 International Research Publications House http://www. irphouse.com /ijict.htm Computer

More information

Fuzzy Description of Skin Lesions

Fuzzy Description of Skin Lesions Fuzzy Description of Skin Lesions Nikolaos Laskaris a, Lucia Ballerini a, Robert B. Fisher a, Ben Aldridge b, Jonathan Rees b a School of Informatics, University of Edinburgh, 10 Crichton Street, Edinburgh,

More information

CS145: INTRODUCTION TO DATA MINING

CS145: INTRODUCTION TO DATA MINING CS145: INTRODUCTION TO DATA MINING 08: Classification Evaluation and Practical Issues Instructor: Yizhou Sun yzsun@cs.ucla.edu October 24, 2017 Learnt Prediction and Classification Methods Vector Data

More information

C. Premsai 1, Prof. A. Kavya 2 School of Computer Science, School of Computer Science Engineering, Engineering VIT Chennai, VIT Chennai

C. Premsai 1, Prof. A. Kavya 2 School of Computer Science, School of Computer Science Engineering, Engineering VIT Chennai, VIT Chennai Traffic Sign Detection Via Graph-Based Ranking and Segmentation Algorithm C. Premsai 1, Prof. A. Kavya 2 School of Computer Science, School of Computer Science Engineering, Engineering VIT Chennai, VIT

More information

Engineering Problem and Goal

Engineering Problem and Goal Engineering Problem and Goal Engineering Problem: Traditional active contour models can not detect edges or convex regions in noisy images. Engineering Goal: The goal of this project is to design an algorithm

More information

Presented at the FIG Congress 2018, May 6-11, 2018 in Istanbul, Turkey

Presented at the FIG Congress 2018, May 6-11, 2018 in Istanbul, Turkey Presented at the FIG Congress 2018, May 6-11, 2018 in Istanbul, Turkey Evangelos MALTEZOS, Charalabos IOANNIDIS, Anastasios DOULAMIS and Nikolaos DOULAMIS Laboratory of Photogrammetry, School of Rural

More information

Adaptive Fuzzy Connectedness-Based Medical Image Segmentation

Adaptive Fuzzy Connectedness-Based Medical Image Segmentation Adaptive Fuzzy Connectedness-Based Medical Image Segmentation Amol Pednekar Ioannis A. Kakadiaris Uday Kurkure Visual Computing Lab, Dept. of Computer Science, Univ. of Houston, Houston, TX, USA apedneka@bayou.uh.edu

More information

Vision Based Parking Space Classification

Vision Based Parking Space Classification 1 Vision Based Parking Space Classification Ananth Nallamuthu, Sandeep Lokala, Department of ECE, Clemson University. Abstract The problem of Vacant Parking space detection from static images using computer

More information

Region-based Segmentation

Region-based Segmentation Region-based Segmentation Image Segmentation Group similar components (such as, pixels in an image, image frames in a video) to obtain a compact representation. Applications: Finding tumors, veins, etc.

More information

A Fully Automatic Random Walker Segmentation for Skin Lesions in a Supervised Setting

A Fully Automatic Random Walker Segmentation for Skin Lesions in a Supervised Setting A Fully Automatic Random Walker Segmentation for Skin Lesions in a Supervised Setting Paul Wighton 1,2,3, Maryam Sadeghi 1,3,TimK.Lee 1,2,3, and M. Stella Atkins 1 1 School of Computing Science, Simon

More information

Performance Evaluation of Basic Segmented Algorithms for Brain Tumor Detection

Performance 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 information

Automated segmentation methods for liver analysis in oncology applications

Automated segmentation methods for liver analysis in oncology applications University of Szeged Department of Image Processing and Computer Graphics Automated segmentation methods for liver analysis in oncology applications Ph. D. Thesis László Ruskó Thesis Advisor Dr. Antal

More information

Trademark Matching and Retrieval in Sport Video Databases

Trademark Matching and Retrieval in Sport Video Databases Trademark Matching and Retrieval in Sport Video Databases Andrew D. Bagdanov, Lamberto Ballan, Marco Bertini and Alberto Del Bimbo {bagdanov, ballan, bertini, delbimbo}@dsi.unifi.it 9th ACM SIGMM International

More information

Texture Image Segmentation using FCM

Texture Image Segmentation using FCM Proceedings of 2012 4th International Conference on Machine Learning and Computing IPCSIT vol. 25 (2012) (2012) IACSIT Press, Singapore Texture Image Segmentation using FCM Kanchan S. Deshmukh + M.G.M

More information

Segmentation Using a Region Growing Thresholding

Segmentation Using a Region Growing Thresholding Segmentation Using a Region Growing Thresholding Matei MANCAS 1, Bernard GOSSELIN 1, Benoît MACQ 2 1 Faculté Polytechnique de Mons, Circuit Theory and Signal Processing Laboratory Bâtiment MULTITEL/TCTS

More information

PSU Student Research Symposium 2017 Bayesian Optimization for Refining Object Proposals, with an Application to Pedestrian Detection Anthony D.

PSU Student Research Symposium 2017 Bayesian Optimization for Refining Object Proposals, with an Application to Pedestrian Detection Anthony D. PSU Student Research Symposium 2017 Bayesian Optimization for Refining Object Proposals, with an Application to Pedestrian Detection Anthony D. Rhodes 5/10/17 What is Machine Learning? Machine learning

More information

Object Purpose Based Grasping

Object Purpose Based Grasping Object Purpose Based Grasping Song Cao, Jijie Zhao Abstract Objects often have multiple purposes, and the way humans grasp a certain object may vary based on the different intended purposes. To enable

More information

Face Detection. Gary Chern, Paul Gurney, and Jared Starman

Face Detection. Gary Chern, Paul Gurney, and Jared Starman Face Detection Gary Chern, Paul Gurney, and Jared Starman. Introduction Automatic face detection is a complex problem in image processing. Many methods exist to solve this problem such as template matching,

More information

CHAPTER 6 DETECTION OF MASS USING NOVEL SEGMENTATION, GLCM AND NEURAL NETWORKS

CHAPTER 6 DETECTION OF MASS USING NOVEL SEGMENTATION, GLCM AND NEURAL NETWORKS 130 CHAPTER 6 DETECTION OF MASS USING NOVEL SEGMENTATION, GLCM AND NEURAL NETWORKS A mass is defined as a space-occupying lesion seen in more than one projection and it is described by its shapes and margin

More information

Learning and Inferring Depth from Monocular Images. Jiyan Pan April 1, 2009

Learning and Inferring Depth from Monocular Images. Jiyan Pan April 1, 2009 Learning and Inferring Depth from Monocular Images Jiyan Pan April 1, 2009 Traditional ways of inferring depth Binocular disparity Structure from motion Defocus Given a single monocular image, how to infer

More information

Computer aided diagnosis of melanoma using Computer Vision and Machine Learning

Computer aided diagnosis of melanoma using Computer Vision and Machine Learning Computer aided diagnosis of melanoma using Computer Vision and Machine Learning Jabeer Ahmed Biomedical Engineering Oregon Health & Science University This paper presents a computer-aided analysis of pigmented

More information

University of Florida CISE department Gator Engineering. Clustering Part 4

University of Florida CISE department Gator Engineering. Clustering Part 4 Clustering Part 4 Dr. Sanjay Ranka Professor Computer and Information Science and Engineering University of Florida, Gainesville DBSCAN DBSCAN is a density based clustering algorithm Density = number of

More information

Study and Analysis of Image Segmentation Techniques for Food Images

Study and Analysis of Image Segmentation Techniques for Food Images Study and Analysis of Image Segmentation Techniques for Food Images Shital V. Chavan Department of Computer Engineering Pimpri Chinchwad College of Engineering Pune-44 S. S. Sambare Department of Computer

More information

CS231A Course Project Final Report Sign Language Recognition with Unsupervised Feature Learning

CS231A Course Project Final Report Sign Language Recognition with Unsupervised Feature Learning CS231A Course Project Final Report Sign Language Recognition with Unsupervised Feature Learning Justin Chen Stanford University justinkchen@stanford.edu Abstract This paper focuses on experimenting with

More information

Mobile Human Detection Systems based on Sliding Windows Approach-A Review

Mobile Human Detection Systems based on Sliding Windows Approach-A Review Mobile Human Detection Systems based on Sliding Windows Approach-A Review Seminar: Mobile Human detection systems Njieutcheu Tassi cedrique Rovile Department of Computer Engineering University of Heidelberg

More information

Processing Missing Values with Self-Organized Maps

Processing Missing Values with Self-Organized Maps Processing Missing Values with Self-Organized Maps David Sommer, Tobias Grimm, Martin Golz University of Applied Sciences Schmalkalden Department of Computer Science D-98574 Schmalkalden, Germany Phone:

More information

Part 3: Image Processing

Part 3: Image Processing Part 3: Image Processing Image Filtering and Segmentation Georgy Gimel farb COMPSCI 373 Computer Graphics and Image Processing 1 / 60 1 Image filtering 2 Median filtering 3 Mean filtering 4 Image segmentation

More information

Pull the Plug? Predicting If Computers or Humans Should Segment Images Supplementary Material

Pull the Plug? Predicting If Computers or Humans Should Segment Images Supplementary Material In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, June 2016. Pull the Plug? Predicting If Computers or Humans Should Segment Images Supplementary Material

More information

Development of an Automated Fingerprint Verification System

Development of an Automated Fingerprint Verification System Development of an Automated Development of an Automated Fingerprint Verification System Fingerprint Verification System Martin Saveski 18 May 2010 Introduction Biometrics the use of distinctive anatomical

More information

Perception. Autonomous Mobile Robots. Sensors Vision Uncertainties, Line extraction from laser scans. Autonomous Systems Lab. Zürich.

Perception. Autonomous Mobile Robots. Sensors Vision Uncertainties, Line extraction from laser scans. Autonomous Systems Lab. Zürich. Autonomous Mobile Robots Localization "Position" Global Map Cognition Environment Model Local Map Path Perception Real World Environment Motion Control Perception Sensors Vision Uncertainties, Line extraction

More information

CS 664 Segmentation. Daniel Huttenlocher

CS 664 Segmentation. Daniel Huttenlocher CS 664 Segmentation Daniel Huttenlocher Grouping Perceptual Organization Structural relationships between tokens Parallelism, symmetry, alignment Similarity of token properties Often strong psychophysical

More information

ECE 172A: Introduction to Intelligent Systems: Machine Vision, Fall Midterm Examination

ECE 172A: Introduction to Intelligent Systems: Machine Vision, Fall Midterm Examination ECE 172A: Introduction to Intelligent Systems: Machine Vision, Fall 2008 October 29, 2008 Notes: Midterm Examination This is a closed book and closed notes examination. Please be precise and to the point.

More information

MELANOMA DETECTION USING HYBRID CLASSIFIER

MELANOMA DETECTION USING HYBRID CLASSIFIER MELANOMA DETECTION USING HYBRID CLASSIFIER D.Selvaraj 1, D.Arul Kumar 2, D.Dhinakaran 3 1,2 Associate Professor, Department of ECE, Panimalar Engineering College, Tamilnadu, (India) 3 Assistant Professor,

More information

International Journal of Modern Engineering and Research Technology

International Journal of Modern Engineering and Research Technology Volume 4, Issue 3, July 2017 ISSN: 2348-8565 (Online) International Journal of Modern Engineering and Research Technology Website: http://www.ijmert.org Email: editor.ijmert@gmail.com A Novel Approach

More information

Color Image Segmentation

Color Image Segmentation Color Image Segmentation Yining Deng, B. S. Manjunath and Hyundoo Shin* Department of Electrical and Computer Engineering University of California, Santa Barbara, CA 93106-9560 *Samsung Electronics Inc.

More information

Clustering Part 4 DBSCAN

Clustering Part 4 DBSCAN Clustering Part 4 Dr. Sanjay Ranka Professor Computer and Information Science and Engineering University of Florida, Gainesville DBSCAN DBSCAN is a density based clustering algorithm Density = number of

More information

Automated Extraction of Buildings from Aerial LiDAR Point Cloud and Digital Imaging Datasets for 3D Cadastre - Preliminary Results

Automated Extraction of Buildings from Aerial LiDAR Point Cloud and Digital Imaging Datasets for 3D Cadastre - Preliminary Results Automated Extraction of Buildings from Aerial LiDAR Point Cloud and Digital Imaging Datasets for 3D Pankaj Kumar 1*, Alias Abdul Rahman 1 and Gurcan Buyuksalih 2 ¹Department of Geoinformation Universiti

More information

Image Mining: frameworks and techniques

Image Mining: frameworks and techniques Image Mining: frameworks and techniques Madhumathi.k 1, Dr.Antony Selvadoss Thanamani 2 M.Phil, Department of computer science, NGM College, Pollachi, Coimbatore, India 1 HOD Department of Computer Science,

More information

MRT based Adaptive Transform Coder with Classified Vector Quantization (MATC-CVQ)

MRT based Adaptive Transform Coder with Classified Vector Quantization (MATC-CVQ) 5 MRT based Adaptive Transform Coder with Classified Vector Quantization (MATC-CVQ) Contents 5.1 Introduction.128 5.2 Vector Quantization in MRT Domain Using Isometric Transformations and Scaling.130 5.2.1

More information

DIGITAL IMAGE ANALYSIS. Image Classification: Object-based Classification

DIGITAL IMAGE ANALYSIS. Image Classification: Object-based Classification DIGITAL IMAGE ANALYSIS Image Classification: Object-based Classification Image classification Quantitative analysis used to automate the identification of features Spectral pattern recognition Unsupervised

More information

Image Segmentation Via Iterative Geodesic Averaging

Image Segmentation Via Iterative Geodesic Averaging Image Segmentation Via Iterative Geodesic Averaging Asmaa Hosni, Michael Bleyer and Margrit Gelautz Institute for Software Technology and Interactive Systems, Vienna University of Technology Favoritenstr.

More information

doi: /

doi: / Yiting Xie ; Anthony P. Reeves; Single 3D cell segmentation from optical CT microscope images. Proc. SPIE 934, Medical Imaging 214: Image Processing, 9343B (March 21, 214); doi:1.1117/12.243852. (214)

More information

CS 223B Computer Vision Problem Set 3

CS 223B Computer Vision Problem Set 3 CS 223B Computer Vision Problem Set 3 Due: Feb. 22 nd, 2011 1 Probabilistic Recursion for Tracking In this problem you will derive a method for tracking a point of interest through a sequence of images.

More information

Computer Vision & Digital Image Processing. Image segmentation: thresholding

Computer Vision & Digital Image Processing. Image segmentation: thresholding Computer Vision & Digital Image Processing Image Segmentation: Thresholding Dr. D. J. Jackson Lecture 18-1 Image segmentation: thresholding Suppose an image f(y) is composed of several light objects on

More information

Image Segmentation. Shengnan Wang

Image Segmentation. Shengnan Wang Image Segmentation Shengnan Wang shengnan@cs.wisc.edu Contents I. Introduction to Segmentation II. Mean Shift Theory 1. What is Mean Shift? 2. Density Estimation Methods 3. Deriving the Mean Shift 4. Mean

More information

Image Segmentation. GV12/3072 Image Processing.

Image Segmentation. GV12/3072 Image Processing. Image Segmentation 1 Recap from last time Samples not squares Sensors are not perfect Quantization hurts Questions? 2 Overview What is image segmentation? Thresholding and thresholding algorithms Performance

More information

CHAPTER 1 INTRODUCTION

CHAPTER 1 INTRODUCTION CHAPTER 1 INTRODUCTION 1.1 Introduction Pattern recognition is a set of mathematical, statistical and heuristic techniques used in executing `man-like' tasks on computers. Pattern recognition plays an

More information

Problem definition Image acquisition Image segmentation Connected component analysis. Machine vision systems - 1

Problem definition Image acquisition Image segmentation Connected component analysis. Machine vision systems - 1 Machine vision systems Problem definition Image acquisition Image segmentation Connected component analysis Machine vision systems - 1 Problem definition Design a vision system to see a flat world Page

More information