This paper describes a simple yet effective algorithm for automatically counting stained breast cancer cell images based on color contents. The procedure for the approach consists of four steps. First, the cancer cell image in red-greenblue (RGB) color space is transformed with Haar wavelet. Second, the wavelet transformed image is changed to gray-scale image. Next, the gray-scale image is segmented with global thresholding using Otsu’s method and morphological operations using opening, region filling, border clearing, and watershed segmentation. Third, the wavelet transformed image is changed to CIEL*a*b* color space. The feature, i.e. average value of b* of each isolated cell, is extracted. Finally, the classification is applied by using the extracted feature. If the average value of b* is positive that indicates yellow, the cancer cell is a positive cell. In addition, if the average value of b* is negative that indicates blue, the cancer cell is a negative cell. Results show that the classified cancer cells by the proposed algorithm are in good agreement with the expert perception. In addition, the algorithm is practical to be used by a pathologist due to its simplicity and effectiveness.
