Image Processing Based Leukemia Cancer Cell Detection

Microscopic pictures are reviewed visually by hematologists and the procedure is tedious and time taking which causes late detection. Therefore automatic image handling framework is required that can overcome related limitations in visual investigation which provide early detection of disease and also type of cancer. The proposed strategy is effectively connected to many numbers of pictures, demonstrating accurate results for changing image standard. Distinctive picture handling calculations, for example, Image enhancement, Clustering, Mathematical process and Labeling are executed utilizing MATLAB. Utilizing a portion of the productive image handling instruments we can recognize and section disease cell. The segmentation helps in knowing the precise size and shape of the cancer cell and the area. First we have utilized image enhancement strategies to improve the quality in terms of contrast and standardize the pixel values in the
picture. After enhancement, segmentation is done to concentrate on area of interest; in this case it is nucleus. K-mean segmentation is used for segmentation. At that point we apply Feature extraction after that we have connected it to classifier to get the desired results as whether the cell is cancerous or not. The algorithm is been utilized on various pictures of the cancerous cell and has constantly given us the correct desired output.

Keywords: K-mean, GLCM, GLDM