Detection of breast masses in mammograms by density slicing and texture flow-field analysis

Abstract

We propose a method for the detection of masses in mammographic images that employs Gaussian smoothing and sub-sampling operations as preprocessing steps. The mass portions are segmented by establishing intensity links from the central portions of masses into the surrounding areas. We introduce methods for analyzing oriented flow-like textural information in mammograms. Features based on flow orientation in adaptive ribbons of pixels across the margins of masses are proposed to classify the regions detected as true mass regions or false-positives (FPs). The methods yielded a mass versus normal tissue classification accuracy represented as an area (Az) of 0.87 under the receiver operating characteristics (ROCs) curve with a dataset of 56 images including 30 benign disease, 13 malignant disease, and 13 normal cases selected from the mini Mammographic Image Analysis Society database. A sensitivity of 81% was achieved at 2.2 FPs/image. Malignant tumor versus normal tissue classification resulted in a higher Az value of 0.9 under the ROC curve using only the 13 malignant and 13 normal cases with a sensitivity of 85% at 2.45 FPs/image. The mass detection algorithm could detect all the 13 malignant tumors successfully, but achieved a success rate of only 63% (19/30) in detecting the benign masses. The mass regions that were successfully segmented were further classified as benign or malignant disease by computing five texture features based on gray-level co-occurrence matrices (GCMs) and using the features in a logistic regression method. The features were computed using adaptive ribbons of pixels across the boundaries of the masses. Benign versus malignant classification using the GCM-based texture features resulted in Az = 0.79 with 19 benign and 13 malignant cases.

Effect of Pixel Resolution on Texture Features of Breast Masses in Mammograms

“…In this work, the width of each ribbon was fixed at 8 mm, regardless of the size of the mass. Mudigonda et al 8 used a ribbon width proportional to the area of the mass; however, the numbers of pixels in such ribbons for small masses could become too low to permit the derivation of reliable GLCMs and texture features.…”

“…Regions in the mammograms related to 111 breast masses, including 65 benign masses and 46 malignant tumors, were identified and the contours of the masses were manually drawn by an expert radiologist specialized in screening mammography. [6][7][8] The sizes of the ROIs with benign masses vary in the range 32-1,207 mm 2 , with an average of 214 mm 2 and a standard deviation of 206 mm 2 . The sizes of the ROIs with malignant tumors vary in the range 34-1,244 mm 2 , with an average of 277 mm 2 and a standard deviation of 285 mm 2 .…”

“…Our previous studies have shown that the GLCM-based texture features computed using a ribbon surrounding the region of interest (ROI) of a mass, also known as the margin of the mass, can lead to higher accuracy of discrimination between benign masses and malignant tumors, as compared to the same texture features computed using the entire ROI. [6][7][8] The present study investigates the effect of pixel size or resolution on texture features, in terms of the accuracy of discrimination between benign masses and malignant tumors.…”

Classification of breast masses using a committee machine of artificial neural networks

“…With the objective to detect and extract the characteristics of lesions, several techniques of image processing have been proposed. [8][9][10][11][12][13][14][15] The artificial neural network ͑ANN͒ is an example of computational intelligence techniques that has been used to classify tumors related to breast cancer. 12,[16][17][18][19][20][21][22][23] Several types of network architecture, such as the multilayer perceptron ͑MLP͒, the single-layer perceptron ͑SLP͒, 19 and ra-dial basis functions ͑RBFs͒ 20 have been used for the classification of breast masses and tumors based on measures of shape, texture, and edge sharpness.…”

Soft Computing Based Decision Making Approach for Tumor Mass Identification in Mammogram

“…The computer aided diagnostics and detection procedures of masses as well as tissues are depicted by researchers in [4,5,15,20,26,28,29] using digital mammogram and other modalities. The present work is continuation of our earlier work based on theory of shape [12][13][14] related to gradation of benignancy of tumor mass in tissue region.…”