scholarly journals Automated Segmentation of Abnormal Tissues in Medical Images

2019 ◽  
Author(s):  
H Khastavaneh ◽  
H Ebrahimpour-komleh
Keyword(s):  
Diagnostic Procedures ◽  
Skin Lesions ◽  
Tissue Type ◽  
Automated Segmentation ◽  
Tissue Segmentation ◽  
Learning Techniques ◽  
Segmentation Methods ◽  
Specific Tissue ◽  
Better Than ◽  
Abnormal Tissue

Nowadays, medical image modalities are almost available everywhere. These modalities are bases of diagnosis of various diseases sensitive to specific tissue type. Usually physicians look for abnormalities in these modalities in diagnostic procedures. Count and volume of abnormalities are very important for optimal treatment of patients. Segmentation is a preliminary step for these measurements and also further analysis. Manual segmentation of abnormalities is cumbersome, error prone, and subjective. As a result, automated segmentation of abnormal tissue is a need. In this study, representative techniques for segmentation of abnormal tissues are reviewed. Main focus is on the segmentation of multiple sclerosis lesions, breast cancer masses, lung nodules, and skin lesions. As experimental results demonstrate, the methods based on deep learning techniques perform better than other methods that are usually based on handy feature engineering techniques. Finally, the most common measures to evaluate automated abnormal tissue segmentation methods are reported.

scholarly journals A General System for Automatic Biomedical Image Segmentation Using Intensity Neighborhoods

2011 ◽  
Vol 2011 ◽  
pp. 1-12 ◽  
Author(s):  
Cheng Chen ◽  
John A. Ozolek ◽  
Wei Wang ◽  
Gustavo K. Rohde
Keyword(s):  
Image Segmentation ◽  
Learning Strategy ◽  
General System ◽  
Training Data ◽  
Tissue Segmentation ◽  
Biomedical Image ◽  
Nuclei Segmentation ◽  
Segmentation Methods ◽  
Microscopy Images ◽  
Better Than

Image segmentation is important with applications to several problems in biology and medicine. While extensively researched, generally, current segmentation methods perform adequately in the applications for which they were designed, but often require extensive modifications or calibrations before being used in a different application. We describe an approach that, with few modifications, can be used in a variety of image segmentation problems. The approach is based on a supervised learning strategy that utilizes intensity neighborhoods to assign each pixel in a test image its correct class based on training data. We describe methods for modeling rotations and variations in scales as well as a subset selection for training the classifiers. We show that the performance of our approach in tissue segmentation tasks in magnetic resonance and histopathology microscopy images, as well as nuclei segmentation from fluorescence microscopy images, is similar to or better than several algorithms specifically designed for each of these applications.

scholarly journals A Novel LSTM Model with Interaction Dual Attention for Radar Echo Extrapolation

2021 ◽  
Vol 13 (2) ◽  
pp. 164
Author(s):  
Chuyao Luo ◽  
Xutao Li ◽  
Yongliang Wen ◽  
Yunming Ye ◽  
Xiaofeng Zhang
Keyword(s):  
Short Term Memory ◽  
Weather Forecast ◽  
Vital Role ◽  
Data Sets ◽  
Short Term ◽  
Learning Techniques ◽  
Radar Echo ◽  
Hidden States ◽  
Better Than

The task of precipitation nowcasting is significant in the operational weather forecast. The radar echo map extrapolation plays a vital role in this task. Recently, deep learning techniques such as Convolutional Recurrent Neural Network (ConvRNN) models have been designed to solve the task. These models, albeit performing much better than conventional optical flow based approaches, suffer from a common problem of underestimating the high echo value parts. The drawback is fatal to precipitation nowcasting, as the parts often lead to heavy rains that may cause natural disasters. In this paper, we propose a novel interaction dual attention long short-term memory (IDA-LSTM) model to address the drawback. In the method, an interaction framework is developed for the ConvRNN unit to fully exploit the short-term context information by constructing a serial of coupled convolutions on the input and hidden states. Moreover, a dual attention mechanism on channels and positions is developed to recall the forgotten information in the long term. Comprehensive experiments have been conducted on CIKM AnalytiCup 2017 data sets, and the results show the effectiveness of the IDA-LSTM in addressing the underestimation drawback. The extrapolation performance of IDA-LSTM is superior to that of the state-of-the-art methods.

Automated segmentation of skin lesions: Modified Fuzzy C mean thresholding based level set method

INMIC ◽  
2013 ◽  
Author(s):  
Ammara Masood ◽  
Adel Ali Al Jumaily ◽  
Azadeh Noori Hoshyar ◽  
Omama Masood
Keyword(s):  
Level Set Method ◽  
Level Set ◽  
Skin Lesions ◽  
Automated Segmentation

scholarly journals A Large-scale Comparison of Cortical and Subcortical Structural Segmentation Methods in Alzheimer’s Disease: a Statistical Approach

2020 ◽  
Author(s):  
Jafar Zamani ◽  
Ali Sadr ◽  
Amir-Homayoun Javadi
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Large Scale ◽  
Automated Segmentation ◽  
Resonance Imaging ◽  
Brain Areas ◽  
Segmentation Methods ◽  
Magnetic Resonance Imaging Mri ◽  
Underlying Mechanisms ◽  
The Brain

AbstractBackgroundAlzheimer’s disease (AD) is a neurodegenerative disease that leads to anatomical atrophy, as evidenced by magnetic resonance imaging (MRI). Automated segmentation methods are developed to help with the segmentation of different brain areas. However, their reliability has yet to be fully investigated. To have a more comprehensive understanding of the distribution of changes in AD, as well as investigating the reliability of different segmentation methods, in this study we compared volumes of cortical and subcortical brain segments, using automated segmentation methods in more than 60 areas between AD and healthy controls (HC).MethodsA total of 44 MRI images (22 AD and 22 HC, 50% females) were taken from the minimal interval resonance imaging in Alzheimer’s disease (MIRIAD) dataset. HIPS, volBrain, CAT and BrainSuite segmentation methods were used for the subfields of hippocampus, and the rest of the brain.ResultsWhile HIPS, volBrain and CAT showed strong conformity with the past literature, BrainSuite misclassified several brain areas. Additionally, the volume of the brain areas that successfully discriminated between AD and HC showed a correlation with mini mental state examination (MMSE) scores. The two methods of volBrain and CAT showed a very strong correlation. These two methods, however, did not correlate with BrainSuite.ConclusionOur results showed that automated segmentation methods HIPS, volBrain and CAT can be used in the classification of AD and HC. This is an indication that such methods can be used to inform researchers and clinicians of underlying mechanisms and progression of AD.

Innovative Scaffold Design for Soft Tissue-to-Bone Interface Tissue Engineering

2011 ◽  
Author(s):  
Siddarth D. Subramony ◽  
Jeffrey P. Spalazzi ◽  
Kristen L. Moffat ◽  
Scott A. Rodeo ◽  
Helen H. Lu
Keyword(s):  
Tissue Engineering ◽  
Soft Tissue ◽  
Acl Reconstruction ◽  
Clinical Success ◽  
Critical Factor ◽  
Tissue Type ◽  
Scaffold Design ◽  
Bone Interface ◽  
Biomimetic Scaffold ◽  
Specific Tissue

Soft tissue-based ACL reconstruction grafts are limited by their inability to reestablish a functional interface with bone tissue[1–2]. The native ACL-bone interface consists of three regions: ligament, fibrocartilage, and bone[3–5]. Graft integration is a critical factor governing its clinical success, and the regeneration of an anatomic interface on synthetic or biological ACL grafts will improve clinical outcome. Our interface tissue engineering effort has focused on biomimetic scaffold design to recapitulate the inherent complexity of the ligament-to-bone interface and ultimately, to guide interface regeneration. To this end, we have designed a tri-phasic scaffold comprised of three distinct yet continuous phases, each designed for the formation of a specific tissue type found at the ACL-to-bone interface, as well as a bi-phasic collar to promote the formation of fibrocartilage on ACL reconstruction grafts and also enhance osteointegration.

scholarly journals Evaluating the Effects of White Matter Multiple Sclerosis Lesions on the Volume Estimation of 6 Brain Tissue Segmentation Methods

2015 ◽  
Vol 36 (6) ◽  
pp. 1109-1115 ◽  
Author(s):  
S. Valverde ◽  
A. Oliver ◽  
Y. Díez ◽  
M. Cabezas ◽  
J.C. Vilanova ◽  
...  
Keyword(s):  
Multiple Sclerosis ◽  
White Matter ◽  
Brain Tissue ◽  
Volume Estimation ◽  
Tissue Segmentation ◽  
Brain Tissue Segmentation ◽  
Segmentation Methods

scholarly journals A HIGH VISUAL QUALITY SPRITE GENERATOR USING INTELLIGENT BLENDING WITHOUT SEGMENTATION MASKS

2006 ◽  
Vol 20 (08) ◽  
pp. 1139-1158 ◽  
Author(s):  
I-SHENG KUO ◽  
LING-HWEI CHEN
Keyword(s):  
Moving Objects ◽  
Automatic Segmentation ◽  
Visual Quality ◽  
Experimental Result ◽  
Feature Point Extraction ◽  
Comparison Results ◽  
Segmentation Methods ◽  
Wu Method ◽  
Better Than

The sprite generator introduced in MPEG-4 blends frames by averaging, which will make places, that are always occupied by moving objects, look blurred. Thus, providing segmented masks for moving objects is suggested. Several researchers have employed automatic segmentation methods to produce moving object masks. Based on these masks, they used a reliability-based blending strategy to generate sprites. Since perfect segmentation is impossible, some ghost-like shadows will appear in the generated sprite. To treat this problem, in this paper, an intelligent blending strategy without needing segmentation masks is proposed. It is based on the fact that for each point in the generated sprite, the corresponding pixels in most frames belong to background and only few belong to moving objects. A counting schema is provided to make only background points participate in average blending. The experimental result shows that the visual quality of the generated sprite using the proposed blending strategy is close to that using manually segmented masks and is better than that generated by Lu-Gao-Wu method. No ghostlike shadows are produced. Furthermore, a uniform feature point extraction method is proposed to increase the precision of global motion estimation, the effectiveness of this part is presented by showing the comparison results with other existing method.

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