Informatics Publishing Limited

R. Sivakumar ¹, Marcus Karnan ²

Affiliations
1 Department of Research and Development, Bharathiyar University, Coimbatore, Tamilnadu, IN
2 Department of Computer Science, Tamilnadu College of Engineering, Coimbatore, Tamilnadu, IN

Abstract

Breast cancer is one of the major causes for the increase in mortality among women, especially in developed countries. Brea cancer is the second most common cancer in women. The presence of microcalcifications in breast tissue is one of the most important signs considered by radiologist for an early diagnosis of breast cancer, which is one of the most common forms of cancer among women. In this paper, detection of microcalcification is performed in two steps: preprocessing& enhancement, segmentation. First, the thresholding algorithm is applied for the breast boundary identification and a new proposed modified tracking algorithm is introduced for pectoral muscle determination in Mammograms. Second, the Genetic Algorithm (GA) and Artificial Bee Colony(ABC) is proposed to automatically detect the breast border and nipple position to identify the suspicious regions on digital mammograms based on asymmetries between left and right breast image. The basic idea of the asymmetry approach is corresponding left and right images are subtracted to extract the suspicious region. The algorithms are tested on 161 patient’s digitized mammograms from MIAS database. In general theproposed GA, ABC and Bilateral algorithms are quite competitive with the other algorithms.

Keywords

Genetic Algorithm (GA), Artificial Bee Colony (ABC), Microcalcifications.

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T. Logeswari ¹, Marcus Karnan ²

Affiliations
1 Dept. of Computer Science, Mother Teresa Women’s University, Kodaikanal, IN
2 Department of Computer Science & Engineering, Tamilnadu College of Engineering, IN

Abstract

Image Segmentation is an important and challenging factor in the medical image segmentation. This paper describes segmentation method consist of two phases. The first phase, describe MRI brain image is acquired from patient’s database, In that film artifact and noise are removed after that HSOM is applied for image segmentation. The HSOM is the extension of the conventional self organizing map used to classify the image row by row. In this lowest level of weight vector, a higher value of tumor pixels, computation speed is achieved by the HSOM with vector quantization.

Keywords

HSOM, Image Analysis, Segmentation,, Tumor Detection.

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