Breast Cancer Diagnosis: Analyzing Texture of Tissue Surrounding Microcalcifications

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1 TITB Breast Cancer Diagnosis: Analyzing Texture of Tissue Surrounding Microcalcifications Anna N. Karahaliou, Ioannis S. Boniatis, Spyros G. Skiadopoulos, Filippos N. Sakellaropoulos, Nikolaos S. Arikidis, Eleni Likaki, George S. Panayiotakis and Lena I. Costaridou Abstract The current study investigates texture properties of the tissue surrounding microcalcification (MC) clusters on mammograms for breast cancer diagnosis. The case sample analyzed consists of 85 dense mammographic images, originating from the Digital Database for Screening Mammography. Mammograms analyzed contain 100 subtle MC clusters (46 benign and 54 malignant). Tissue surrounding MCs is defined on original and wavelet decomposed images, based on a redundant discrete wavelet transform. Grey level texture and wavelet coefficient texture features at three decomposition levels are extracted from surrounding tissue regions of interest (ST-ROI). Specifically, grey level first order statistics, grey level cooccurrence matrices features and Laws texture energy measures are extracted from original image ST-ROIs. Wavelet coefficient first order statistics and wavelet coefficient co-occurrence matrices features are extracted from subimages ST-ROIs. The ability of each feature set in differentiating malignant from benign tissue is investigated using a probabilistic neural network (PNN). Classification outputs of most discriminating feature sets are combined using a majority voting rule. The proposed combined scheme achieved an area under Receiver Operating Characteristic curve (A z ) of Results suggest that MCs surrounding tissue texture analysis can contribute to computeraided diagnosis of breast cancer. Index Terms Breast cancer, computer aided diagnosis, mammography, texture analysis, tissue surrounding microcalcifications Manuscript received April 30, 2007; revised October 20, 2007 and December 20, 2007.This work was supported by the European Social Fund (ESF), Operational Program for Educational and Vocational Training II (EPEAEK II), and particularly the Program PYTHAGORAS I (B ). Asterisk indicates corresponding author. *L. I. Costaridou is with the Department of Medical Physics, School of Medicine, University of Patras, Patras, Greece ( ; fax: ; costarid@upatras.gr). A. N. Karahaliou, I. S. Boniatis, S. G. Skiadopoulos, F. N. Sakellaropoulos and N. S. Arikidis are with the Department of Medical Physics, School of Medicine, University of Patras, Patras, Greece ( karahaliou.a@med.upatras.gr; iboniat@yahoo.com; skiado@med.upatras.gr; phisakel@med.upatras.gr; arikidis@med.upatras.gr). G. S. Panayiotakis is with the Department of Medical Physics, School of Medicine, University of Patras, Patras, Greece, and also with the Medical Radiation Physics Unit, University Hospital of Patras, Patras, Greece ( panayiot@upatras.gr). E. Likaki is with the Department of Radiology, University Hospital of Patras, Patras, Greece ( likaki@med.upatras.gr). M I. INTRODUCTION AMMOGRAPHY is currently the most effective imaging modality for early detection of breast cancer. However, in case of dense breast parenchyma, both detection and characterization tasks are highly challenged [1]-[3]. Microcalcification (MC) clusters are important indicators of malignancy, and they appear in 30-50% of the mammographically diagnosed cases [4]. Computer Aided (CA) detection systems for MCs have demonstrated high performance. CA diagnosis of MCs remains a challenging task [1], [5], due to similarity in morphology of both malignant and benign clusters and low distinguishability from dense parenchyma. Various CA diagnosis algorithms have been proposed for automated discrimination between benign and malignant MC clusters, based either on image features extracted by radiologists [6]-[8], or on computer extracted image features [9]-[19], [23]-[25]. Reported studies employing computer extracted image features have followed two approaches. The first approach focuses on computer extracted morphology features of individual MCs or of MC clusters [9]-[15]. The second approach employs texture features extracted from ROIs containing MC clusters [16]-[19]. While a comprehensive review of the proposed CA schemes can be found elsewhere [1], [20], in the following paragraph some representative studies in terms of the features used are presented. Shen et al. [9] proposed a set of shape features of individual MCs, achieving 100% overall accuracy in classification of 143 individual MCs. Jiang et al. [10] used 8 features of MC clusters in a neural network classifier, and achieved an area under Receiver Operating Characteristics (ROC) curve (A z )=0.92 in a dataset of 53 patients. Veldkamp et al. [11] used cluster shape, position and distribution features for classification of MCs. A patient-based classification was performed by combining information of both views (mediolateral oblique-mlo and craniocaudal-cc), achieving A z =0.83. Leichter et al. [12] used features reflecting the internal architecture within a MC cluster and stepwise discriminant analysis for optimum feature selection and classification, achieving an A z =0.98 in a dataset of 134 cases. Lee et al. [13] designed a shape recognition-based neural network for capturing geometric information of MCs. They achieved sensitivity 86.1% and specificity 71.4% in a Copyright (c) 2008 IEEE. Personal use of this material is permitted.

2 TITB dataset of 40 mammograms. Kallergi et al. [14] used morphological features of individual MCs and MC clusters for the classification of 100 cases, achieving high performance (Az=0.98) when incorporating age in their classification scheme. Papadopoulos et al. [15] used features characterizing individual MCs and MC clusters; they compared a rule-based system, an artificial neural-network and a support vector machine with the latter performing better (A z =0.81). Dhawhan et al. [16] used second order statistics (cooccurrence matrices based) and wavelet-based Energy and Entropy extracted from ROIs containing the MCs, as well as first order statistics from segmented MCs, and obtained an A z =0.86 for classification of 191 difficult-to-diagnose cases. Chan et al. [17] used co-occurrence matrices-based features extracted from ROIs containing the MCs and achieved an A z =0.84 in a dataset of 145 cases; combining textural and morphological features they achieved an A z =0.89, which increased to 0.93 when averaging discriminant score from all views of the same cluster (100% sensitivity with 50% specificity). Kramer and Aghdasi [18] used second order statistics from original and wavelet decomposed images, as well as wavelet-based Energy and Entropy, extracted from ROIs containing the MCs. They compared the performance of both a k-nearest Neighbor (knn) classifier and a neural network classifier, with the latter achieving a 94.8% overall classification accuracy. Zadeh et al. [19] compared the performance of four feature sets: co-occurrence matricesbased features, MCs shape descriptors, wavelet (Energy and Entropy) and multi-wavelet features, with the latter achieving an A z =0.89. The success of the morphology-based schemes depends on the robustness of the MC segmentation algorithm used, which is highly challenged in case of dense breast parenchyma [1], [21], [22]. Texture-based schemes by analyzing ROIs containing MCs, seem to overcome this limitation. However, this approach may introduce bias as the MC, a tiny deposit of calcium in breast tissue, can neither be malignant nor benign. This characterization refers rather to the tissue surrounding the MC. This tissue is also the one subjected to pathoanatomical and immunochemistry analysis to derive a benign or a malignant outcome. Thiele et al. [23] investigated this hypothesis by extracting textural features (co-occurrence matrices-based and fractal geometry) from tissue surrounding MCs as depicted on digital scout views from stereotactic biopsy procedure. They analyzed 54 cases and achieved 85% classification accuracy using Linear and Logistic Discriminant analysis. In our previous efforts towards MCs surrounding tissue analysis on conventional mammographic views only grey level texture was exploited [24], [25]. The aim of the current work is to investigate multiscale texture properties of tissue surrounding MCs for breast cancer diagnosis. Texture features are extracted from original and multiscale representation of the tissue surrounding the MCs, aiming at capturing coarse and fine tissue alterations that may associated with a malignant or a benign biological process. Specifically, we examined 3 feature sets from original images and wavelet coefficients statistics from three scales. The extracted feature sets are compared by means of their ability in discriminating malignant from benign tissue using a Probabilistic Neural Network (PNN) classifier. Performance of individual feature sets and the combined scheme is achieved by means of ROC analysis. II. MATERIALS AND METHODS The steps of the proposed method are illustrated in Fig. 1 and described in detail in the following subsections. Fig. 1. Flowchart of the proposed classification scheme. A. Case Sample The case sample consists of 85 mammographic images of 52 patients, originating from the Digital Database for Screening Mammography (DDSM) [26], digitized with the LUMISYS 200 scanner at 12-bits pixel depth and 50 µm spatial resolution. Mammograms selected contain in total 100 MC clusters (54 malignant and 40 benign biopsy proven, and 6 benign without call-back) and correspond to heterogeneously dense and extremely dense breast parenchyma. The DDSM database provides also a malignancy assessment for each MC cluster, in a 5-point scale, according to the ACR BIRADS TM specifications [27]. The distribution of the case sample analyzed with respect to malignancy assessment is provided in Fig. 2. Fig. 2. Distribution of the case sample with respect to malignancy assessment 1: negative, 2: benign, 3: probably benign, 4: suspicious abnormality and 5: highly suggestive of malignancy.

3 This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. TITB B. Preprocessing A preprocessing step is performed in order to facilitate the subsequent MC segmentation task. Specifically, images are preprocessed using a wavelet-based spatially adaptive method for mammographic contrast enhancement [28]-[30]. Preprocessing was performed on 600x600 pixels ROIs containing the MCs. Fig. 3 presents a ROI containing the MC cluster in original image (a) and the corresponding processed ROI (b). A region growing method [9] was employed on processed ROI to segment MC areas, with seed points and tolerance parameters defined by an experienced radiologist. Fig. 3. (a) ROI (600x600 pixels) of original mammogram containing a MC cluster (DDSM: volume cancer_09, case B_3406, RIGHT_CC). (b) Processed ROI. (c) Surrounding tissue ROI (ST-ROI) on original image indicated by black rectangle of 128x128 pixels. Excluded MC areas are labeled black. (d) Magnified ST-ROI. Borders of excluded MC areas are indicated with solid line. C. Defining Tissue Surrounding MCs The segmented MC areas are used to define pixels coordinates on original image that do not participate in the subsequent texture feature extraction. The remaining tissue area, subjected to texture feature extraction, is defined as the Surrounding Tissue ROI (ST-ROI). Fig. 3(c) shows a ST-ROI on original image data (indicated by black rectangle of 128x128 pixels), where the excluded MC areas are labeled black. Fig. 3(d) depicts a magnified ST-ROI, where the borders of the excluded MC areas are outlined. Pixels inside the outlined MC areas do not participate in feature extraction; pixels outside the outlined MC areas are considered for feature extraction. In order to extract Laws texture energy measures [31] the ST-ROI has to be modified. The ROI containing MCs on the original image (Fig. 3(a)), is filtered by the Laws kernels. To deal with contaminated pixels adjacent to MC areas, the segmented MC areas are dilated with a structure element of size proportional to Laws kernel length and subsequently used to define the modified ST-ROI. 3 To extract wavelet coefficient texture features, the ST-ROI is modified as following. The ROI containing MCs on the original image (Fig. 3(a)), is decomposed up to three levels (dyadic scales), employing the non-subsampled biorthogonal discrete wavelet transform whose wavelet filter is the first order derivative B-spline [32]. At each scale, approximation and gradient magnitude coefficients (based on the horizontal and vertical detail coefficients) are considered for texture analysis. To deal with contaminated pixels adjacent to MC areas, the segmented MC areas are dilated in proportion to the filter lengths used to compute approximation and gradient magnitude coefficients. The dilated MC areas are subsequently used to define the modified ST-ROIs on the approximations at scales 1, 2 (Fig. 4(a,b)) and on the gradient magnitude coefficients at scales 2, 3 (Fig. 4(c,d)). Fig. 4. ST-ROIs (128x128 pixels) on approximation and magnitude coefficients of Fig. 3(a). (a) Approximation 1st scale. (b) Approximation 2nd scale. (c) Magnitude 2nd scale. (d) Magnitude 3rd scale. Borders of excluded MC areas are indicated with solid line. D. Texture Analysis of Tissue Surrounding MCs Texture analysis of ST-ROIs was restricted in a rectangle area of 128x128 pixels, aligned at the center of the cluster. For clusters larger that 128x128 pixels, more ST-ROIs were considered (up to three ST-ROIs with less than 30% overlap) to cover the entire cluster area. The texture feature values extracted from multiple ST-ROIs, covering a large cluster area, were averaged. 1) Grey Level Texture (GLT) Features The following feature sets are investigated: i) Grey Level First Order Statistics (GLFOS): GLFOS provide different statistical properties (4 statistical moments) of the intensity histogram of an image [33]. They depend only on individual pixel values and not on the interaction or cooccurrence of neighboring pixel values. In this study, four GLFOS were calculated: Mean, Standard Deviation, Kurtosis and Skewness. ii) Grey Level Co-occurrence Matrices (GLCM) features: The co-occurrence matrix is a well-established robust

4 TITB statistical tool for extracting second order texture information from images [34], [35]. This matrix characterizes the spatial distribution of grey levels in an image. An element at location (i,j) of the co-occurrence matrix signifies the joint probability density of the occurrence of grey levels i and j in a specified orientation θ and specified distance d from each other. Thus, for different θ and d values, different matrices are generated. In this study, four matrices corresponding to four different directions (θ=0, 45, 90 and 135 ) and one distance (d=1 pixel), were computed for each selected ST-ROI. Fifteen features were derived from each GLCM. Specifically, the features studied are: Angular Second Moment, Entropy, Contrast, Local Homogeneity, Correlation, Shade, Prominence, Variance, Sum Average, Sum Entropy, Difference Entropy, Sum Variance and Difference Variance, Information Measure of Correlation 1 and Information Measure of Correlation 2. Four values were obtained for each feature corresponding to the four matrices. The mean and range of these four values were calculated, comprising a total of 30 GLCM features. iii) Laws Texture Energy Measures (LTEMs): Textural features were extracted based on the method proposed by Laws [31]. According to this approach, textural features are extracted from images that have previously been filtered by each one of the 25 Laws masks or kernels. These filtered images are characterized as Texture Energy (TE) images. Averaging the TE images corresponding to symmetrical kernels (such as R5L5 and L5R5), and taking into account that 20 out of 25 kernels are symmetric one to each other, 15 TR images were produced (R stands for Rotational invariance ). From each one of the 15 TR images, 5 first-order statistics (mean, standard deviation, range, skewness and kurtosis) were computed (i.e., 5 LTEMs subcategories, each one containing 15 features), giving in total 75 LTEMs. 2) Wavelet Coefficient Texture (WCT) Features. The following feature sets are investigated: i) Wavelet Coefficient First Order Statistics (WCFOS): Two first order statistics features were calculated: Entropy and Energy [16], [18], [19]. ii) Wavelet Coefficient Co-occurrence Matrices (WCCM) features: in proportion to GLCM, co-occurrence matrices can also be used to characterize coefficients second order statistics. In a similar manner, an element at location (i,j) of the coefficient co-occurrence matrix signifies the joint probability density of the occurrence of wavelet coefficients i and j in a specified orientation θ and specified distance d from each other. The same 30 features extracted from GLCMs are also extracted from the WCCMs. Prior to feature extraction, subimage discretization was performed. The following wavelet coefficients are considered for texture analysis: the approximation coefficients of 1 st scale, approximation and magnitude coefficients of 2 nd scale and magnitude coefficients of 3 rd scale. The magnitude coefficients of the 1 st scale are not analyzed as it is expected to be noise contaminated [32]. The approximation coefficients of the 3 rd scale are not analyzed as smoothing due to corresponding low-pass filtering would almost eliminate texture. Features extracted from each of the three scales are considered separately for the subsequent classification task. Thus, 3 feature sets are generated corresponding to the decomposition scales. Specifically, the first set consists of 32 features extracted from the approximation coefficients of 1 st scale. The second set consists of 64 features extracted from the approximation and the magnitude coefficients of the 2 nd scale (32 features extracted from approximation and 32 features extracted from magnitude coefficients). The third set consists of 32 features extracted from the magnitude coefficients of 3 rd scale. All extracted textural features were normalized to zero mean and unit standard deviation [36] and subsequently used for classification. E. Classification of Tissue Surrounding MCs A PNN was used for the classification of tissue surrounding MCs. PNN encompasses both the Bayes classification approach and the Parzen s estimators of probability density functions [37]. In the current study, the discriminant function for class j was given by: 1 g j ( x) = p / 2 p (2π ) σ N N j j i= 1 e 2 2 x xi / 2σ where p is the dimensionality (number of features) of the input pattern, sigma (σ) is a smoothing parameter ranging from 0 to 1, x i is the ith training input pattern, x is the unknown pattern to be classified and N j is the number of patterns forming the class j. PNN has a four-layer architecture: an input layer, a pattern layer, a summation layer and an output layer. The input layer stores temporarily each pattern vector, which is fed to the network. The number of neurons (nodes) that structure the input layer is equal to the dimensionality (p) of the input pattern. Each input pattern is mapped to each one of the neurons of the pattern layer. Each neuron in the pattern layer represents a training pattern. The summation layer has one neuron for each class, and implements the summation term of (1) for the outputs of the patterns corresponding to the class. The output layer contains one neuron and assigns the input vector to a class by implementing a classification rule. The Decision function for classification is given by: (1) Decision = gm ( x) gb ( x) > 0 (2) where g M (x) is the discriminant function for class M (Malignant) and g B (x) is the discriminant function for class B (Benign). If Decision is greater than zero, the unknown pattern x is assigned to class M; otherwise the unknown pattern is assigned to class B. The discriminating ability of each feature set (6 sets are considered) was investigated by using all individual features of each set as inputs to the classifier. For each textural feature set a best feature set was selected with respect to overall accuracy achieved, employing an exhaustive search procedure [36]. Specifically, for each feature set, combinations of 2-6 features were investigated and the combination of minimum number of features that provided the highest overall accuracy

5 TITB was selected. In the case of LTEMs (75 features in total) the application of the exhaustive search over the entire set of features would be impractical. Thus, the exhaustive search procedure was initially performed for each LTEMs subcategory (mean, standard deviation, range, skewness and kurtosis). Subsequently, features selected from the five LTEMs subcategories were subjected to exhaustive search to derive the most discriminating LTEMs feature set. The same approach was followed in case of the 2 nd scale WCT features (64 features in total). The exhaustive search procedure was initially performed separately for the approximation and the magnitude coefficients. Following, features selected from both approximation and magnitude coefficients were subjected to exhaustive search to derive the most discriminating 2 nd scale WCT feature set. The training and testing of the classifier, for each textural feature set, was performed using the leave-one-out methodology [36]. Classification outputs of the most discriminating feature sets, from both GLT and WCT features, are combined using a majority voting rule [38]. In this approach, the unknown sample is assigned to the class of the majority of the classification outputs. The performance of the classifier for each textural feature set and the combined classification scheme was evaluated by means of ROC analysis [39], [40]. F. ROC Analysis To obtain a ROC curve for classification based on each individual textural feature set, malignancy thresholds (confidence threshold values) have to be set; above the malignancy threshold a sample is considered malignant and below the threshold is considered benign. In the current study, the Decision value given in (2) was considered as a measure of malignancy for each sample. Thus, we partitioned the range of Decision values over the whole case sample in ten values to obtain ten malignancy thresholds, and to subsequently derive ten raw data points of a ROC curve. To obtain a ROC curve for the combined classification scheme, we defined as malignancy threshold for each sample the number of malignant classification outputs provided by the textural feature sets considered in the scheme. The threshold was changed from -1 up to K (K is the maximum number of malignant classification outputs) with step 1, to derive raw data points of the ROC curve [19]. In order to provide a baseline reference of the performance of the proposed surrounding tissue texture-based classification approach, a ROC curve was also generated for the DDSM assessment of malignancy following the method described in [41]. As in the case sample analyzed 4 malignancy ratings exist, three malignancy thresholds are rendered, providing 3 points of the ROC curve, which in addition to its extreme points (beginning and end) yield in total 5 raw data points. The ROCKIT program (Metz CE, University of Chicago, IL) was used for generation of ROC curves, and calculation of the area under the estimated ROC curve (A z ), Standard Error (SE) and the asymmetric 95% Confidence Interval (CI) [39, 40]. Differences in A z values were statistically analyzed using area test (z-score). Derived two-tailed values of p<0.05 indicate statistically significant differences between classification schemes. III. RESULTS Table I provides the optimum combination of features, for each texture feature set, selected with respect to overall classification accuracy achieved by means of the exhaustive search procedure. Corresponding values of the smoothing parameter (σ) and classification accuracy (%) are also provided. Mean and Skewness included in the best feature set of the GLFOS. In the dataset analyzed, malignant cases had higher mean grey level value as compared to the benign ones, which could be justified by the fact that the occurrence of breast cancers is greater in areas of mammographically dense tissue [42]. The best feature set of GLCM included four features. Malignant cases presented lower values in the 4 selected GLCMs features, as compared to the benign cases. It seems that malignant tissue is characterized by reduced amount of local variation and reduced randomness, possibly associated with a malignant biological process. Among LTEM, the combination of five features yielded the highest overall classification accuracy and provided the best feature set. Features included in WCT best feature sets are differentiated with respect to frequency band (scale). However, the Entropy feature appears in all subsets, indicating high discriminating ability. TABLE I OPTIMUM COMBINATION OF FEATURES FOR EACH TEXTURE FEATURE SET GLFOS GLCM LTEM Feature set Best Feature Set σ (1 st scale) (2 nd scale) (3 rd scale) Mean Skewness Mean Shade Range Contrast Range Difference Entropy Range Difference Variance Kurtosis from S5L5TR Skewness from E5L5TR Mean from R5L5TR Mean from L5L5TR STD from S5L5TR Entropy (A) Mean Local Homogeneity (A) Mean Difference Entropy (A) Range Difference Variance (A) Entropy (M) Mean Prominence (M) Range Variance (M) Mean Sum Average (A) Entropy (M) Mean Entropy (M) Mean Sum Entropy (M) Mean Shade (M) Range Sum Average (M) Range IMC2 (M) Accuracy (%) σ = smoothing parameter, M = magnitude subimage, A = approximation subimage, STD = standard deviation, IMC2 = Information Measure of Correlation 2. Table II provides results of the classification performance of individual best feature sets, of the combined scheme

6 TITB considered and the DDSM assessment of malignancy, by means of the area under ROC curve (A z ). Corresponding SE and CI values are also provided. Among GLT feature sets, LTEM performed better and statistically significant as compared to GLFOS and GLCM (p<0.05). Concerning WCT features sets, the 3 rd scale feature set outperformed 2 nd and 1 st scale feature sets, although not statistically significant (p>0.05). GLFOS demonstrated the poorest performance (0.763±0.051) among all feature sets considered, and thus was not included in the combined classification scheme. The combined scheme performed best (0.989±0.008) and statistically significant (p<0.05) as compared to individual feature sets and the DDSM assessment of malignancy (0.838±0.051). TABLE II CLASSIFICATION PERFORMANCE OF INDIVIDUAL FEATURE SETS AND OF THE COMBINED SCHEME BY MEANS OF THE AREA UNDER ROC CURVE (A Z ) AND CORRESPONDING STANDARD ERROR (SE) AND ASSYMETRIC 95% CONFIDENCE INTERVAL (CI) VALUES Feature set A z SE CI GLFOS (0.652, 0.851) GLCM (0.718, 0.885) LTEM (0.827, 0.951) (0.779, 0.923) (1 st scale) (0.773, 0.921) (2 nd scale) (0.844, 0.961) (3 rd scale) Combination (0.960, 0.998) DDSM assessment (0.720, 0.918) Fig. 5 presents ROC curves corresponding to individual feature sets, the combined classification scheme and the DDSM assessment of malignancy. As it is observed the combined scheme can achieve a sensitivity of 96.3% at specificity level 91.3% (accuracy: 94%), whereas the DDSM assessment of malignancy achieves the same sensitivity at low specificity level of 10.9% (accuracy: 57%). Fig. 5. ROC curves corresponding to individual feature sets, the combined classification scheme and the DDSM assessment of malignancy. IV. DISCUSSION AND CONCLUSIONS In this study texture properties of the tissue surrounding MCs are investigated both on original and mutliscale representations for breast cancer diagnosis. Extending our previous efforts in MCs surrounding tissue analysis [24], [25] where only grey level texture features were exploited, wavelet coefficients texture features are also investigated to capture both fine and coarse tissue alterations that may be associated with a malignant underlying biological process. To achieve MCs surrounding tissue analysis, the tissue is appropriately defined on original and multiscale image representations. The extracted feature sets are compared by means of their ability in discriminating malignant from benign tissue using a PNN classifier. Final classification is obtained by combining classification outputs of most discriminating grey level and wavelet coefficients feature subsets. Performance evaluation was performed by means of ROC analysis. High performance was obtained by the proposed combined scheme (A z =0.989), enhanced as compared to our previous efforts (A z =0.963) [25], attributed to the combination of both grey level and wavelet coefficients texture. The method was demonstrated on a difficult dataset (dense breast parenchyma), as further reflected by the corresponding DDSM malignancy assessment performance. As compared to GLFOS and GLCM, WCFOS and WCCM demonstrated high discriminating performance that was consistent across the three scales analyzed. LTEMs demonstrated a comparable performance to wavelet coefficients feature sets, suggesting that image energy provided by filter-based approaches to texture is of high discriminating ability. While a direct comparison with other texture based classification studies is not possible due to different classification algorithms, textural features and datasets (MC clusters subtlety and breast density types) utilized, the proposed surrounding tissue analysis seems to perform better [16]-[19]. As compared to morphology-based schemes the proposed method demonstrated a comparable performance to [9], [12], and [14], with the advantage of being less affected by the presence of dense breast parenchyma. In the current study the leave-one-out methodology was used for training/testing the classifier, which is preferred for small size datasets [36]. However, correlation between the data of the same patient may have favourably biased the reported classification performance. Future efforts should consider validation over a larger dataset employing the leaveone-patient-out scheme [11] to improve reliability of the estimated classification performance. While in the current study texture analysis was restricted in the tissue surrounding MCs in a 6.4x6.4 mm 2 area (128x128 pixels), also adopted by Thiele et al. [23], the impact of the ST-ROI size on classification performance should be analyzed. Finally, the hypothesis of texture analysis of tissue surrounding MCs for breast cancer diagnosis must be further exploited by investigating the correlation between the extracted textural features and pathoanatomical findings [43].

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