Fractional meshfree linear diffusion method for image enhancement and segmentation for automatic tumor classification Abstract   Computer aided diagnostic (CAD) models have shown outstanding performance in identifying many kind of diseases. Tumor identification is one of the most useful application of CAD model. Benign and malignant are two categories of tumor cells. Both categories share some textural features due to which tumor classification becomes complex and difficult task. In the present manuscript, a novel CAD model is being presented for classifying tumor cells automatically. The proposed model has been divided into four modules (image enhancement, segmentation, feature extraction and classification). All the modules are equally important in tumor classification. The manuscript focuses on effect of first two modules namely image enhancement and segmentation on tumor classification. In the present work, a novel linear fractional meshfree partial differential equation (FPDE) based image enhancement method has been proposed to improve quality of the images. The proposed enhancement model is able to preserve fine details in smooth areas also nonlinearly increases high frequency information. A novel fractional meshfree segmentation method has been proposed to extract tumor region. It has been found that the method is able segment tumor region more accurately. Thirteen textural features have been used for training and testing. Support vector machine (SVM) classifier has been used to classify extracted tumor region. Quantitative analysis of proposed image enhancement, segmentation and CAD model have been done with other popular models. Higher performance has been observed using proposed models.

A Backgroundbased Data Enhancement Method for Lymphoma Segmentation in 3D PET Images Due to the poor resolution and low signaltonoise ratio in PET images, and especially to the wide variation in size, shape, site and SUV value among different patients or even for the same patient, lymphoma segmentation in 3D PET Images is still a challenging task in the field of medical image processing. In this work, a novel nonself backgroundbased data enhancement method is proposed for the deep learningbased lymphoma segmentation problem. Firstly, a lymphoma pool with 1991 lymphoid lesions is created. Then, some lymphomas from the lymphoma pool are randomly selected and integrated into their nonself images of the patients according to their respective coordinates when training networks. Finally, a series of comparison experiments among various network models and methods are conducted to verify the effectiveness of the proposed method. The results indicated that the proposed method was promising, and could obtain better comprehensive performance than other methods without any data enhancements for the lymphoma segmentation problems.