Accurate Breast Contour Detection Algorithms in Digital Mammogram

Abstract

Computer Aided Diagnosis (CAD) systems have improved diagnosis of abnormalities in mammogram images. The principal feature within the breast region is the breast contour. Extraction of the breast region and delineation of the breast contour allows the search for abnormalities to be limited to the region of the breast without undue influence from the background of the mammogram. After performing an essential pre-processing step to suppress artifacts and accentuate the breast region, the exact breast region as the region of interest (ROI), has to be segmented. In this paper we present a fully automated segmentation and boundary detection method for mammographic images.In this research paper we have proposed a new homogeneity enhancement process namely Binary Homogeneity Enhancement Algorithm (BHEA) for digital mammogram. This is followed by a novel approach for edge detection (EDA) and finally obtaining the breast boundary by using our proposed Breast Border Boundary Enhancement Algorithm. This composite method have been implemented and applied to mini-MIAS, one of the most well-known mammographic database consisting of 322 medio-lateral oblique (MLO) view obtained via a digitization procedure. To demonstrate the capability of our segmentation algorithm it was extensively tested on mammograms using ground truth images and quantitative metrics to evaluate its performance characteristics. The experimental results indicate that the breast boundary regions were extracted accurately characterize the corresponding ground truth images. The algorithm is fully autonomous, and is able to preserve, skin and nipple (if in profile), a task very few existing mammogram segmentation algorithms can claim.

High-performance near-infrared imaging for breast cancer detection

“…[15][16][17][18][20][21][22][23][24][25][26][27][28] Also, by using this technique with the help of advanced digital image processing, we would be able to distinguish and differentiate between different grade levels of tumor and cancerous cells. The novelty of the current work lies in several areas as mentioned in this scenario; first, on the imaging front, we have been able to achieve high-resolution photothermal imaging of tumor with size less than 2 cm 3 using NIR laser.…”

“…20,21,28,29 The edges identified by edge detection are often disconnected. Region boundaries and edges are closely related, since there is often a sharp adjustment in intensity at the region boundaries.…”

“…22,23,28,29 It is robust to noise and implements a Gaussian function to smooth the image and to obtain the magnitude and orientation of the gradient for each pixel. 22,23,28,29 It is robust to noise and implements a Gaussian function to smooth the image and to obtain the magnitude and orientation of the gradient for each pixel.…”

Ascertaining Abnormal Regions in Mammogram Images using Gravitational Search Local Map View Technique

“…Breast shape is one of the biggest single highlights of mammogram. Indra Kanta Maitra et al [11] proposed automated segment algorithm and boundary detection method to extract breast region. Three methods are combined to segment the breast area.…”

Image Compression Using Local Map View Technique

“…The important part on an image is the breast outskirt, also called the skin-air interface, or bosom limit. The breast form can be acquired by compression in the [15] imageinto breast and non-breast areas. The extracted breast contour should adequately model the soft tissue/air interface and preserve the nipple in profile.…”