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Automatic Segmentation of Liver from Abdominal Computed Tomography Images Using Energy Feature

Computers, Materials & Continua 2021年第4期67卷 709-722页

作者： Prabakaran rajamanickam Shiloah Elizabeth Darmanayagam sunil retmin raj cyril raj University Department of Computer Science Engineering Anna UniversityChennai600025India University Department of Information Technology MIT CampusChennai600044India

Liver Segmentation is one of the challenging tasks in detecting and classifying liver tumors from Computed Tomography(CT)*** segmentation of hepatic organ is more intricate task,owing to the fact that it possesses a sizeable quantum of *** paper proposes an algorithm for automatic seed point selection using energy feature for use in level set algorithm for segmentation of liver region in CT *** effectiveness of the method can be determined when used in a model to classify the liver CT images as tumorous or *** involves segmentation of the region of interest(ROI)from the segmented liver,extraction of the shape and texture features from the segmented ROI and classification of the ROIs as tumorous or not by using a classifier based on the extracted *** this work,the proposed seed point selection technique has been used in level set algorithm for segmentation of liver region in CT scans and the ROIs have been extracted using Fuzzy C Means clustering(FCM)which is one of the algorithms to segment the *** dataset used in this method has been collected from various repositories and scan *** outcome of this proposed segmentation model has reduced the area overlap error that could offer the intended accuracy and *** gives better results when compared with other existing *** execution in short span of time is another advantage of this method which in turns helps the radiologist to ascertain the abnormalities instantly.

关键词： Liver segmentation automatic seed point tumor segmentation classification fuzzy C means clustering

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Semantic Segmentation of Lumbar Vertebrae Using Meijering U-Net(MU-Net)on Spine Magnetic Resonance Images

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Computer Modeling in Engineering & Sciences 2025年第1期142卷 733-757页

作者： Lakshmi S V V Shiloah Elizabeth Darmanayagam sunil retmin raj cyril Department of Computer Science and Engineering College of Engineering GuindyAnna UniversityChennai600025India Department of Information Technology Madras Institute of TechnologyAnna UniversityChrompetChennai600044India

Lower back pain is one of the most common medical problems in the world and it is experienced by a huge percentage of people *** to its ability to produce a detailed view of the soft tissues,including the spinal cord,nerves,intervertebral discs,and vertebrae,Magnetic Resonance Imaging is thought to be the most effective method for imaging the *** semantic segmentation of vertebrae plays a major role in the diagnostic process of lumbar *** is difficult to semantically partition the vertebrae in Magnetic Resonance Images from the surrounding variety of tissues,including muscles,ligaments,and intervertebral discs.U-Net is a powerful deep-learning architecture to handle the challenges of medical image analysis tasks and achieves high segmentation *** work proposes a modified U-Net architecture namely MU-Net,consisting of the Meijering convolutional layer that incorporates the Meijering filter to perform the semantic segmentation of lumbar vertebrae L1 to L5 and sacral vertebra ***-colour mask images were generated and used as ground truth for training the *** work has been carried out on 1312 images expanded from T1-weighted mid-sagittal MRI images of 515 patients in the Lumbar Spine MRI Dataset publicly available from Mendeley *** proposed MU-Net model for the semantic segmentation of the lumbar vertebrae gives better performance with 98.79%of pixel accuracy(PA),98.66%of dice similarity coefficient(DSC),97.36%of Jaccard coefficient,and 92.55%mean Intersection over Union(mean IoU)metrics using the mentioned dataset.

关键词： Computer aided diagnosis(CAD) magnetic resonance imaging(MRI) semantic segmentation lumbar vertebrae deep learning U-Net model

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