COMPUTER-AIDED DETECTION OF CLUSTERED CALCIFICATION USING IMAGE MORPHOLOGY
Ariya Namvong · 2012
The presence of microcalcification clusters in the mammogram image is a significant sign for the breast cancer at an early stage. The early detection increases the chance for successful treatment and complete recovery of the patient [1]. At present, the detection of microcalcification is still difficult because of their fuzzy nature, low contrast and low distinguish-ability from their surroundings [2, 3]. The interpretations of their presence are very difficult because of their morphological features. Microcalcifications are very small, typically between 0.1 and 1.0 mm, which means that they can be easily overlooked by a radiologist [4]. The purpose of this paper is to identify the location of suspicious areas to assist radiologists for diagnosis. The proposed method is divided into four steps: (a) image preprocessing (b) image enhancement using image morphology (c) individual calcification detection using intensity threshold, where pixels with high intensity are considered as suspicious pixels; and finally (d) clustered calcification detection, where suspicious pixels in close proximity are grouped into clusters. 17 images with calcification marked by expert radiologists from MiniMIAS database [5] were tested to evaluate the detection of the proposed method. From the tested images that contain 3 types of breast tissue consisting of fatty, fatty-glandular and dense-glandular. There are 2 types of calcifications presented in the tested image, benign and malignant. From 17 images with calcification marked from MiniMIAS, all calcifications locations were correctly detected. At this point, this is just a preliminary experiment. The author cannot claim that this method can successfully detect for all mammographic images. Larger image database is needed to improve the proposed method. Request for more mammographic images from Thailand Breast Center is in processing.