Unsupervised Segmentation Algorithms' Implementation in ITK for Tissue Classification via Human Head MRI Scans
Tissue classification is one of the significant tasks in the field of biomedical image analysis. Magnetic Resonance Imaging (MRI) is of great importance in tissue classification especially in the areas of brain tissue classification which is able to recognize anatomical areas of interest such as surgical planning, monitoring therapy, clinical drug trials, image registration, stereotactic neurosurgery, radiotherapy etc. The task of this paper is to implement different unsupervised classification algorithms in ITK and perform tissue classification (white matter, gray matter, cerebrospinal fluid (CSF) and background of the human brain). For this purpose, 5 grayscale head MRI scans are provided. In order of classifying brain tissues, three algorithms are used. These are: Otsu thresholding, Bayesian classification and Bayesian classification with Gaussian smoothing. The obtained classification results are analyzed in the results and discussion section.
Code (0)
등록된 구현이 없습니다.
Tasks
ClassificationGeneral ClassificationImage RegistrationSimilar Papers 제목 키워드 기반
Methods for Segmentation and Classification of Digital Microscopy Tissue Images
High-resolution microscopy images of tissue specimens provide detailed information about the morphology of normal and diseased tissue. Image analysis of tissue morphology can help cancer researchers develop a better unde…
ClassificationGeneral Classificationimage-classificationImage Classification+1Deep Spectral Methods for Unsupervised Ultrasound Image Interpretation
Ultrasound imaging is challenging to interpret due to non-uniform intensities, low contrast, and inherent artifacts, necessitating extensive training for non-specialists. Advanced representation with clear tissue structu…
AnatomyClusteringDeep LearningA novel unsupervised covid lung lesion segmentation based on the lung tissue identification
This study aimed to evaluate the performance of a novel unsupervised deep learning-based framework for automated infections lesion segmentation from CT images of Covid patients. In the first step, two residual networks w…
Lesion SegmentationSegmentationContext Driven Label Fusion for segmentation of Subcutaneous and Visceral Fat in CT Volumes
Quantification of adipose tissue (fat) from computed tomography (CT) scans is conducted mostly through manual or semi-automated image segmentation algorithms with limited efficacy. In this work, we propose a completely u…
Computed Tomography (CT)Image SegmentationSegmentationSemantic SegmentationUnsupervised Tissue Segmentation via Deep Constrained Gaussian Network
Tissue segmentation is the mainstay of pathological examination, whereas the manual delineation is unduly burdensome. To assist this time-consuming and subjective manual step, researchers have devised methods to automati…
Segmentation