105 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
Comparative assessment of texture features for the identification of cancer in ultrasound images: a review
In this paper, we review the use of texture features for cancer detection in Ultrasound (US) images of breast, prostate, thyroid, ovaries and liver for Computer-Aided Diagnosis (CAD) systems. This paper shows that texture features are a valuable tool to extract diagnostically relevant information from US images. This information helps practitioners to discriminate normal from abnormal tissues. A drawback of some classes of texture features comes from their sensitivity to both changes in image resolution and grayscale levels. These limitations pose a considerable challenge to CAD systems, because the information content of a specific texture feature depends on the US imaging system and its setup. Our review shows that single classes of texture features are insufficient, if considered alone, to create robust CAD systems, which can help to solve practical problems, such as cancer screening. Therefore, we recommend that the CAD system design involves testing a wide range of texture features along with features obtained with other image processing methods. Having such a competitive testing phase helps the designer to select the best feature combination for a particular problem. This approach will lead to practical US based cancer detection systems which de- liver real benefits to patients by improving the diagnosis accuracy while reducing health care cost
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
Automatic ROI detection and classification of the Achilles tendon ultrasound images
Ultrasound (US) imaging plays an important role in medical
imaging technologies. It is widely used because of its ease of use
and low cost compared to other imaging techniques. Specifically,
ultrasound imaging is used in the detection of the Achilles Tendon
(AT) pathologies as it detects important details. For example, US
imaging is used for AT rupture that affects about 1 in 5,000
people worldwide. Decision support systems are important in
medical imaging, as they assist radiologist in detecting probable
diagnoses and lesions. The work presented in this paper concerns
the development of a software application to detect changes in the
AT ultrasound images and subsequently classify them into normal
or abnormal. We propose an approach that fully automates the
detection for the Region of Interest (ROI) in ultrasound AT
images. The original image is divided into six blocks with 1 cm
size in each direction. The blocks lie inside the vulnerable area
considered as our ROI. The proposed system achieved an
accuracy of 97.21%
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
Recommended from our members
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
- …