81 research outputs found
Evaluation of Statistical Features for Medical Image Retrieval
In this paper we present a complete system allowing the classification of medical images in order to detect possible diseases present in them. The proposed method is developed in two distinct stages: calculation of descriptors and their classification. In the first stage we compute a vector of thirty-three statistical features: seven are related to statistics
of the first level order, fifteen to that of second level where thirteen are calculated by means of co-occurrence matrices and two with absolute gradient; the last thirteen finally are calculated using run-length matrices. In the second phase, using the descriptors already calculated, there is the actual image classification. Naive Bayes, RBF, Support VectorMa-
chine, K-Nearest Neighbor, Random Forest and Random Tree classifiers are used. The results obtained from the proposed system show that the analysis carried out both on textured and on medical images lead to have a high accuracy
PCA-SVM based CAD System for Focal Liver Lesions using B-Mode Ultrasound Images
The contribution made by texture of regions inside and outside of the lesions in classification of focal liver lesions (FLLs) is investigated in the present work. In order to design an efficient computer-aided diagnostic (CAD) system for FLLs, a representative database consisting of images with (1) typical and atypical cases of cyst, hemangioma (HEM) and metastatic carcinoma (MET) lesions, (2) small as well as large hepatocellular carcinoma (HCC) lesions and (3) normal (NOR) liver tissue is used. Texture features are computed from regions inside and outside of the lesions. Feature set consisting of 208 texture features, (i.e. 104 texture features and 104 texture ratio features) is subjected to principal component analysis (PCA) for finding the optimal number of principal components to train a support vector machine (SVM) classifier for the classification task. The proposed PCA-SVM based CAD system yielded classification accuracy of 87.2% with the individual class accuracy of 85%, 96%, 90%, 87.5% and 82.2% for NOR, Cyst, HEM, HCC and MET cases respectively. The accuracy for typical, atypical, small HCC and large HCC cases is 87.5%, 86.8%, 88.8%, and 87% respectively. The promising results indicate usefulness of the CAD system for assisting radiologists in diagnosis of FLLs.Defence Science Journal, 2013, 63(5), pp.478-486, DOI:http://dx.doi.org/10.14429/dsj.63.395
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Diagnosis of liver disease by computer- assisted imaging techniques: A literature review
Copyright © 2022 The authors. Diagnosis of liver disease using computer-aided detection (CAD) systems is one of the most efficient and cost-effective methods of medical image diagnosis. Accurate disease detection by using ultrasound images or other medical imaging modalities depends on the physician's or doctor's experience and skill. CAD systems have a critical role in helping experts make accurate and right-sized assessments. There are different types of CAD systems for diagnosing different diseases, and one of the applications is in liver disease diagnosis and detection by using intelligent algorithms to detect any abnormalities. Machine learning and deep learning algorithms and models play also a big role in this area. In this article, we tried to review the techniques which are utilized in different stages of CAD systems and pursue the methods used in preprocessing, extracting, and selecting features and classification. Also, different techniques are used to segment and analyze the liver ultrasound medical images, which is still a challenging approach to how to use these techniques and their technical and clinical effectiveness as a global approach
Computer-Assisted Algorithms for Ultrasound Imaging Systems
Ultrasound imaging works on the principle of transmitting ultrasound waves into the body and
reconstructs the images of internal organs based on the strength of the echoes. Ultrasound imaging
is considered to be safer, economical and can image the organs in real-time, which makes it widely
used diagnostic imaging modality in health-care. Ultrasound imaging covers the broad spectrum
of medical diagnostics; these include diagnosis of kidney, liver, pancreas, fetal monitoring, etc.
Currently, the diagnosis through ultrasound scanning is clinic-centered, and the patients who are
in need of ultrasound scanning has to visit the hospitals for getting the diagnosis. The services of
an ultrasound system are constrained to hospitals and did not translate to its potential in remote
health-care and point-of-care diagnostics due to its high form factor, shortage of sonographers, low
signal to noise ratio, high diagnostic subjectivity, etc. In this thesis, we address these issues with an
objective of making ultrasound imaging more reliable to use in point-of-care and remote health-care
applications. To achieve the goal, we propose (i) computer-assisted algorithms to improve diagnostic
accuracy and assist semi-skilled persons in scanning, (ii) speckle suppression algorithms to improve
the diagnostic quality of ultrasound image, (iii) a reliable telesonography framework to address
the shortage of sonographers, and (iv) a programmable portable ultrasound scanner to operate in
point-of-care and remote health-care applications
Thin Cap Fibroatheroma Detection in Virtual Histology Images Using Geometric and Texture Features
Atherosclerotic plaque rupture is the most common mechanism responsible for a majority
of sudden coronary deaths. The precursor lesion of plaque rupture is thought to be a thin
cap fibroatheroma (TCFA), or “vulnerable plaque”. Virtual Histology-Intravascular Ultrasound
(VH-IVUS) images are clinically available for visualising colour-coded coronary artery tissue.
However, it has limitations in terms of providing clinically relevant information for identifying
vulnerable plaque. The aim of this research is to improve the identification of TCFA using VH-IVUS
images. To more accurately segment VH-IVUS images, a semi-supervised model is developed by
means of hybrid K-means with Particle Swarm Optimisation (PSO) and a minimum Euclidean
distance algorithm (KMPSO-mED). Another novelty of the proposed method is fusion of different
geometric and informative texture features to capture the varying heterogeneity of plaque components
and compute a discriminative index for TCFA plaque, while the existing research on TCFA detection
has only focused on the geometric features. Three commonly used statistical texture features are
extracted from VH-IVUS images: Local Binary Patterns (LBP), Grey Level Co-occurrence Matrix
(GLCM), and Modified Run Length (MRL). Geometric and texture features are concatenated in
order to generate complex descriptors. Finally, Back Propagation Neural Network (BPNN), kNN
(K-Nearest Neighbour), and Support Vector Machine (SVM) classifiers are applied to select the best
classifier for classifying plaque into TCFA and Non-TCFA. The present study proposes a fast and
accurate computer-aided method for plaque type classification. The proposed method is applied to 588 VH-IVUS images obtained from 10 patients. The results prove the superiority of the proposed
method, with accuracy rates of 98.61% for TCFA plaque.This research was funded by Universiti Teknologi Malaysia (UTM) under Research University
Grant Vot-02G31, and the Ministry of Higher Education Malaysia (MOHE) under the Fundamental Research Grant
Scheme (FRGS Vot-4F551) for the completion of the research. The work and the contribution were also supported
by the project Smart Solutions in Ubiquitous Computing Environments, Grant Agency of Excellence, University
of Hradec Kralove, Faculty of Informatics and Management, Czech Republic (under ID: UHK-FIM-GE-2018).
Furthermore, the research is also partially supported by the Spanish Ministry of Science, Innovation and
Universities with FEDER funds in the project TIN2016-75850-R
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