Computer aided decision system for early detection of breast cancer.

BACKGROUND & OBJECTIVES: Mammography is currently the method of choice for early detection of breast cancer in women. However, the interpretation of mammograms is largely based on radiologist's opinion. In this study an attempt is made to develop an image processing algorithm for the detection of microcalcifications and also a computer based decision system for early detection of breast cancer.The proposed method deals with a novel approach for the development of a computer aided decision (CAD) system for early detection of breast cancer by mammogram image analysis. METHODS: The proposed method employs simple thresholding the region of interest and the use of filters for clear identification of microcalcifications. The method suggested for the detection of microcalcifications from a mammogram image segmentation and analysis was tested over several images taken from mini-MIAS (Mammogram Image Analysis Society, UK) database. The algorithm was implemented using Metlab codes programming and hence can work effectively on a simple personal computer with digital mammogram as stored data for analysis. RESULTS: The algorithm works faster so that any radiologist can take a clear decision about the appearance of microcalcifications by visual inspection of digital mammograms. The performance of the algorithm was tested over several images and the validation of results by visual inspection were done by an expert radiologist. Also, the system has given good detection rate as high as 78 percent. The performance analysis of the CAD algorithm was done by receiver operating characteristics (ROC) plot. INTERPRETATION & CONCLUSION: The CAD system suggested here was capable of detecting microcalcifications with a high detection rate, and thus could be used for early detection of breast cancer.

Detection of breast cancer by mammogram image segmentation.

An important approach for describing a region is to quantify its structure content. In this paper the use of functions for computing texture based on statistical measures is prescribed. MPM (Maximizer of the posterior margins) algorithm is employed. The segmentation based on texture feature would classify the breast tissue under various categories. The algorithm evaluates the region properties of the mammogram image and thereby would classify the image into important segments. Images from mini-MIAS data base (Mammogram Image Analysis Society database (UK)) have been considered to conduct our experiments. The segmentation thus obtained is comparatively better than the other normal methods. The validation of the work has been done by visual inspection of the segmented image by an expert radiologist. This is our basic step for developing a computer aided detection (CAD) system for early detection of breast cancer.