Precise segmentation of the liver is critical for computeraided diagnosis such as pre-evaluation of the liver for living donor-based transplantation surgery. This task is challenging due to the weak boundaries of organs, countless anatomical variations, and the complexity of the background. Computed tomography (CT) scanning and magnetic resonance imaging (MRI) images have different parameters and settings. Thus, images acquired from different modalities differ from one another making liver segmentation challenging task. We propose an efficient liver segmentation with the combination of holistically-nested edge detection (HED) and Mask-region-convolutional neural network (R-CNN) to address these challenges. The proposed HED-Mask R-CNN approach is based on effective identification of edge map from multimodal images. The proposed system firstly applies a preprocessing step of image enhancement to get the 'primal sketches' of the abdomen. Then the HED network is applied to enhanced CT and MRI modality images to get better edge map. Finally, the Mask R-CNN is used to segment the liver from edge map images. We used a dataset of 20 CT patients and 9 MR patient from the CHAOS challenge. The system is trained on CT and MRI images separately and then converted to 2D slices. We significantly improved the segmentation accuracy of CT and MRI images on a database with Dice value of 0.94 for CT, 0.89 for T2-weighted MRI and 0.91 for T1-weighted MRI.
In this document, we propose a novel palm vein<em> </em>recognition system using open source hardware and software. We have developed an alternative preprocessing and feature extraction technique. The proposed system is built on Raspberry Pi using OpenCV 2.4.12. The palm vein image is cropped to Region of Interest(ROI) to reduce the computational time in real time systems and then preprocessed to enhance the vein pattern visibility and to extract more number of key points using SIFT algorithm. Then the descriptors are stored in a dictionary like codebook file during training. Later the descriptors are tested with unknown patterns. The clustering is based on K-means algorithm and classification is done using Support Vector Machines (SVM).
The most common brain disorders due to abnormal burst of electrical discharges are termed as Epileptic seizures. This work proposes an efficient approach to extract the features of epileptic seizures by decomposing EEG into band limited signals termed as IMF's by empirical decomposition EMD. Huang Hilbert Transform is applied on these IMF's for calculating Instantaneous frequencies and are classified using artificial neural network trained by Back propagation algorithm. The results indicate an accuracy of 97.87%. The algorithm is implemented using Verilog HDL on Zynq 7000 family FPGA evaluation board using Xilinx vivado 2015.2 version.
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