The early detection of lymphoma in patients can greatly increase the probability of recovery. Centroblasts of lymphoma are conventionally identified under microscopes by expert doctor's manually. cytogenetics and immunopheno typing are currently used. A novel method has been proposed to recognize centroblast (CB) cells from non centroblast (non-CB) cells for computer assisted evaluation of lymphocyte centroblasts. The novel aspects is to identify the centroblast cells with prior information. Geometric and texture features are extracted from the input image for identification and classification. Important geometric features like area, boundingbox, convex area, perimeter, major-axis, minor-axis, solidity are extracted. Texture features are extracted using Log-Gabor filter. Combined features of texture and morphology improve the performance of the system significantly. Supervised support vector machine (SVM) classifier is used to classify centroblast (CB) and non-CB cell.

The idiosyncrasies of the medical profession makes medical image mining a challenge. Image mining deals with the extraction of implicit knowledge, image data relationship, or other patterns not explicitly stored in the images. It is very difficult to determine the exact number of microorganisms under the microscope in the presence of a human expert in conventional methods. An automated tool for fast recognition of microbes is needed to examine the medical data before it expires. Digital image processing is an integral part of microscopy. Automated color image segmentation for bacterial image is proposed to classify the bacteria into two broad categories of gram images. Edge detection algorithm with 8 neighbor-connectivity contour is used. Bacterial morphological, geometric features extracted from microscopy images are used for classification and clustering. The potential and distinguished features are extracted from each bacterial cell. Experimental results with self organizing map shows that the bacterial cluster patterns obtained are better than the statistical approach.