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A 3D coupled electromagnetic thermal model was developed using COMSOL 4.0 to predict the electromagnetic field distribution and temperature profile in pathological tissue samples immersed in a reagent inside the oven cavity. The effect of the volume of reagent on the mean heating rate and heating uniformity within the tissue sample was investigated. Also, the effect of using a water load, as a method of temperature control, is emphasized. A well insulated K type thermocouple connected to a PC is used for model validation. Good agreement is found between experimental and simulated temperature profiles. Results show that as the volume of reagent increases, the mean heating rate decreases and temperature homogeneity increases. Also, it is possible to minimize overshooting temperature values inside the tissue sample and enhance tissue uniformity by about 27% using 100 ml of water load and 42.26% using 150 ml. Domestic microwave oven is a low cost economical tool that can speed up tissue processing steps. Achieving uniform heating inside the microwave oven is the key factor for improving workflow inside pathological labs and maintaining tissue quality and integrity.

The diffuse lymphoma is a malignant tumor of lymphoid tissues. It is associated with abnormal, unlimited and uncontrolled proliferation of lymphoid cells. Until now, expert pathologists have identified diffuse lymphoma cells disease manually. This paper introduces automatic system with a friendly user interface to differentiate between the categories of the diffuse lymphoma cells. This research is based on the morphological features such as size, perimeter and circularity. The cell size is a critical element in the classification of diffuse lymphoma according to international formulation standards. Therefore, the applied procedures identify lymphoid cell population in digital microscopic images.The cells are classified using their morphological data according to the characteristics of each cell such as: circularity, perimeter, area, and color density. The number of cells is taken into consideration in the developed approach. Image processing techniques are applied to digital microscopic images to measure morphological parameters and to overcome image problems such as overlapping and cell distortion that affect the sensitivity of the measured data. The developed procedures help the pathologists to come to a decision regarding the classification of diffuse lymphoma. Moreover, it can be used to train medical students and young pathologists.

The recognition of the acute Leukemia blast cells in colored microscopic images is a challenging task. Segmentation is the essential step for image analysis and image processing. In this paper, an algorithm is presented that consists of panel selection followed by segmentation using K-means clustering then a refinement process. This algorithm was applied on public dataset designed for testing segmentation techniques. The results were compared with two different segmentation techniques developed by other researchers on the same data set. Our algorithm results in a sensitivity of 97.4 % and specificity of 98.1%. The developed algorithm was tested to another dataset of samples extracted from patients in local hospitals. The algorithm results in sensitivity of 100%, Specificity of 99.747% and accuracy of 99.7617%. The results were approved by expert pathologists. General TermsPattern Recognition, Image processing and segmentation.