Medical and health arena is advanced in recent years with the technological influence especially using image processing techniques and algorithms. Biomedical Image processing resolves many cons of manual disease recognition. In this paper we have depicted the automated clinical diagnosis for tumor detection based on segmentation of CT scan images towards lungs cancer, ovary cancer and liver cancer. Tumor is an exceptional expansion generated by human cells reproducing themselves in an unconstrained manner. Accurate detection of size and location of tumour plays a vital role in the diagnosis of tumor. Clustering plays a specific role in Image object segmentation both in Gray and RGB based Bio-medical images. We have taken different CT scanned Image of three main sources of diseases processing for detection of tumor based on three major life killer disease with the steps of image processing keeping prominence on noise removal, contrast enhancement by stretching.

Gout is a metabolic disorder due to the deposition of uric acid crystals within articular or periarticular tissues. Uricase (urate oxidase) catalyzes the oxidation of less water soluble uric acid (7 mg/dl) to a compound allantoin which is more water soluble(11g/L at 40°C) resulting into the ease of excretion of uric acid. The objective of the work was to develop a new method for screening of microbes for uricase production and estimation of uricase thereof. This was achieved by utilizing the fact that uric acid dissolves on being acted upon by uricase. The proposed method is a novel, inexpensive, simple and sensitive technique for screening and estimation of uricase. Biomass and uricase production at different stages of microbial growth curve for the uricase producing microbe was studied. Effects of different medium components affecting uricase production by microbes were studied using Placket Burman statistical design. Addition of uric acid in the nutrient medium was found to be effective in increasing the uricase production by microbes growing in the medium.

Introduction: Correct cell enumeration and differential analysis of body fluids are important in the diagnosis and management of several diseases. Currently, microscopic analysis is still considered the "gold standard". The introduction of automated analysis has reduced interoperator variability, improved turnaround time and precision. The present study was designed to determine the shelf life and appropriate anticoagulant for automated cell counter and to compare manual and automated cell count in ascitic fluid. Methods: We examined total 250 ascitic fluid samples. Total and differential cell counting of each sample has been conducted with the Sysmex XT-4000i and the manual method (Neubauer chamber). Linearity, carryover, precision and correlation were also assessed. Results:The precision analysis of random sample with high cell count indicated that the Sysmex XT-4000i demonstrated good precision for RBC, WBC, MN#, PMN# at 0 and 6 hour in all vials resulted in CVs <0.012%. Carryover effect was negligible for both WBC and RBC count in ascitic fluid. It never exceeded 0.180%. Sysmex XT-4000i showed significant positive correlation for WBC count in plain vial (r=0.984, p<0.001s), EDTA vial (r= 0.998, p<0.001s), PT vial (r=0.958,