Realization methods of computer-aided diagnosis system of medical images

2019 ◽  
Vol 13 ◽  
Author(s):  
Muhammad Aqeel Ashraf ◽  
Shahreen Kasim
Keyword(s):  
Data Model ◽  
Object Segmentation ◽  
Medical Images ◽  
Computer Aided Diagnosis ◽  
Support Vector ◽  
Cad System ◽  
Existing Problems ◽  
Vector Machines ◽  
Computer Aided ◽  
Aided Diagnosis

: In this paper, medical images are used to realize the computer-aided diagnosis (CAD) system which develops targeted solutions to existing problems. Relying on the Mi COM platform, this system has collected and collated cases of all kinds, based on which a unified data model is constructed according to the gold standard derived by deducting each instance. Afterwards, the object segmentation algorithm is employed to segment the diseased tissues. Edge modification and feature extraction are performed for the tissue block segmented. The features extracted are classified by applying support vector machines or the Naive Bayesian classification algorithm. From the simulation results, the CAD system developed in this paper allows realization of diagnosis and treatment and sharing of data resources.

scholarly journals A computer-aided diagnosis system for lung nodule detection in pulmonary CT images based on LS-SVM and wavelet transformation

2021 ◽  
Author(s):  
Omid Talakoub
Keyword(s):  
Wavelet Transformation ◽  
Ct Images ◽  
Computer Aided Diagnosis ◽  
Regions Of Interest ◽  
Support Vector ◽  
Cad Systems ◽  
Cad System ◽  
Computer Aided ◽  
Spherical Structures ◽  
Aided Diagnosis

One of the most important areas of biomedical engineering is medical imaging. Fully automated schemes are currently being explored as Computer-Aided Diagnosis (CAD) systems to provide a second opinion to medical professionals; of these systems, abnormal region detector in medical images is one of the most critical CAD systems in development. The primary motivation in using these systems is due to the fact that reading an enormous number of images is a time-consuming task for the radiologist. This task can be sped up by using a CAD system which highlights abnormal regions of interest. Low false positive rates and high sensitivity are essential requirement[s] of such a system. The initial requirement of processing any organ is an accurate segmentation of the target of interest in the images. A segmentation method based on the wavelet transformation is proposed which accurately extracts lung regions in the thoracic CT images. After this step, an Aritifical Intelligence system, known as Least Squares Support Vector Machine (LS-SVM), is employed to classify nodules within the regions of interest. It is a well known fact that the lung nodules, except the pleural nodules, are mostly spherical structures whereas other structures including blood vessels are shaped as other structures such as tubular. Therfore, an enhancment filter is developed in which spherical structures are accentuated. Processing three different real databases revealed that the proposed system has reached the objective of a CAD system to provide reliable opinion for the doctors in the diagnosis fashion.

Multi-Class SVMs Based on Priority for Computer-Aided Diagnosis of Lung Nodules in CT Images

2011 ◽  
Vol 291-294 ◽  
pp. 2742-2745
Author(s):  
Qing Zhu Wang ◽  
Xin Zhu Wang ◽  
Ji Song Bie ◽  
Bin Wang
Keyword(s):  
Support Vector Machines ◽  
Sensitivity And Specificity ◽  
Ct Images ◽  
Computer Aided Diagnosis ◽  
Least Square ◽  
Support Vector ◽  
Lung Nodules ◽  
Vector Machines ◽  
Computer Aided ◽  
Aided Diagnosis

A priority based ‘One against all (OAA)’ Multi-class Least Square-Support Vector Machines is designed to remove the unclassifiable regions exist in basic OAA. POAA develops the sensitivity and specificity in Computer-aided Diagnosis (CAD) for detection of lung nodules.

scholarly journals A computer-aided diagnosis system for lung nodule detection in pulmonary CT images based on LS-SVM and wavelet transformation

2021 ◽  
Author(s):  
Omid Talakoub
Keyword(s):  
Wavelet Transformation ◽  
Ct Images ◽  
Computer Aided Diagnosis ◽  
Regions Of Interest ◽  
Support Vector ◽  
Cad Systems ◽  
Cad System ◽  
Computer Aided ◽  
Spherical Structures ◽  
Aided Diagnosis

One of the most important areas of biomedical engineering is medical imaging. Fully automated schemes are currently being explored as Computer-Aided Diagnosis (CAD) systems to provide a second opinion to medical professionals; of these systems, abnormal region detector in medical images is one of the most critical CAD systems in development. The primary motivation in using these systems is due to the fact that reading an enormous number of images is a time-consuming task for the radiologist. This task can be sped up by using a CAD system which highlights abnormal regions of interest. Low false positive rates and high sensitivity are essential requirement[s] of such a system. The initial requirement of processing any organ is an accurate segmentation of the target of interest in the images. A segmentation method based on the wavelet transformation is proposed which accurately extracts lung regions in the thoracic CT images. After this step, an Aritifical Intelligence system, known as Least Squares Support Vector Machine (LS-SVM), is employed to classify nodules within the regions of interest. It is a well known fact that the lung nodules, except the pleural nodules, are mostly spherical structures whereas other structures including blood vessels are shaped as other structures such as tubular. Therfore, an enhancment filter is developed in which spherical structures are accentuated. Processing three different real databases revealed that the proposed system has reached the objective of a CAD system to provide reliable opinion for the doctors in the diagnosis fashion.

scholarly journals A Computer-Aided Diagnosis System for Dynamic Contrast-Enhanced MR Images Based on Level Set Segmentation and ReliefF Feature Selection

2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
Zhiyong Pang ◽  
Dongmei Zhu ◽  
Dihu Chen ◽  
Li Li ◽  
Yuanzhi Shao
Keyword(s):  
Feature Selection ◽  
Level Set ◽  
Computer Aided Diagnosis ◽  
Support Vector ◽  
Svm Classifier ◽  
Segmentation Method ◽  
Cad System ◽  
Computer Aided ◽  
Resampling Method ◽  
Aided Diagnosis

This study established a fully automated computer-aided diagnosis (CAD) system for the classification of malignant and benign masses via breast magnetic resonance imaging (BMRI). A breast segmentation method consisting of a preprocessing step to identify the air-breast interfacing boundary and curve fitting for chest wall line (CWL) segmentation was included in the proposed CAD system. The Chan-Vese (CV) model level set (LS) segmentation method was adopted to segment breast mass and demonstrated sufficiently good segmentation performance. The support vector machine (SVM) classifier with ReliefF feature selection was used to merge the extracted morphological and texture features into a classification score. The accuracy, sensitivity, and specificity measurements for the leave-half-case-out resampling method were 92.3%, 98.2%, and 76.2%, respectively. For the leave-one-case-out resampling method, the measurements were 90.0%, 98.7%, and 73.8%, respectively.

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