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computer-aided-diagnosis
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Novel margin features for mammographic mass classification
, Article Proceedings - 2012 11th International Conference on Machine Learning and Applications, ICMLA 2012 ; Volume 2 , 2012 , Pages 139-144 ; 9780769549132 (ISBN) ; Zarghami, A ; Manzuri Shalmani, M. T ; Sharif University of Technology
2012
Abstract
Computer-Aided Diagnosis (CAD) systems are widely used for detection of various kinds of abnormalities in mammography images. Masses are one type of these abnormalities which are mostly characterized by their margin and shape. For classification of masses proper features are needed to be extracted. However, the number of well-known features for describing margin is much fewer than geometrical, shape, and textural ones. In addition, most of the existing margin features are highly dependent on segmentation accuracy. In this work, new features for describing margin of masses are presented which can handle inaccuracies in segmentation. These features are obtained from a set of waveforms by...
WN-based approach to melanoma diagnosis from dermoscopy images
, Article IET Image Processing ; Volume 11, Issue 7 , 2017 , Pages 475-482 ; 17519659 (ISSN) ; Azarianpour, S ; Zekri, M ; Emre Celebi, M ; Sadri, S ; Sharif University of Technology
Abstract
A new computer-aided diagnosis (CAD) system for detecting malignant melanoma from dermoscopy images based on a fixed grid wavelet network (FGWN) is proposed. This novel approach is unique in at least three ways: (i) the FGWN is a fixed WN which does not require gradient-type algorithms for its construction, (ii) the construction of FGWN is based on a new regressor selection technique: D-optimality orthogonal matching pursuit (DOOMP), and (iii) the entire CAD system relies on the proposed FGWN. These characteristics enhance the integrity and reliability of the results obtained from different stages of automatic melanoma diagnosis. The DOOMP algorithm optimises the network model approximation...
A novel method for segmentation of leukocyte nuclei based on color transformation
, Article 26th National and 4th International Iranian Conference on Biomedical Engineering, ICBME 2019, 27 November 2019 through 28 November 2019 ; 2019 , Pages 213-217 ; 9781728156637 (ISBN) ; Maheri, J ; Behroozi, H ; Kolahdoozi, M ; Sharif University of Technology
Institute of Electrical and Electronics Engineers Inc
2019
Abstract
Acute lymphoblastic leukemia is one of the most common hematologic malignancies among children, caused by uncontrolled growth of leukocytes. Since the main hallmarks of the disease is not specific, a considerable number of patients have been being misdiagnosed. Early diagnosis of the disease is usually made by morphological investigation of leukocytes under microscope. In light of the facts that decrease in cytoplasm-to-nucleus ratio is one of the main indicators of cancerous cells, and an accurate segmentation phase will lead to extraction of representative features, segmentation step is acknowledged as being crucial in design of a computer aided diagnosis (CAD). Previous researches have...
Diagnosis of schizophrenia from R-fMRI data using Ripplet transform and OLPP
, Article Multimedia Tools and Applications ; Volume 79, Issue 31-32 , 2020 , Pages 23401-23423 ; Kalbkhani, H ; Shayesteh, M. G ; Sharif University of Technology
Springer
2020
Abstract
Schizophrenia is a severe brain disease that influences the behaviour and thought of person. These effects may fail in achieving the expected levels of interpersonal, academic, or occupational functioning. Although the underlying mechanism is not yet clear, the early detection of schizophrenia is an attractive and challenging research area. There are differences in brain connections of patients and healthy people. This study presents a new computer-aided diagnosis (CAD) method to diagnose schizophrenia (SZ) patients from normal control (NC) people by using the rest-state functional magnetic resonance imaging (R-fMRI) data. fMRI data has a huge dimension, and extracting efficient features is...
Improving the 3D Segmentation of Nodules in Lung CT Images
, M.Sc. Thesis Sharif University of Technology ; Jamzad, Mansour (Supervisor) ; Beigy, Hamid (Co-Supervisor)
Abstract
Lung cancer is one of the most common types of cancers, and its early diagnosis can save many lives. Due to the high number of computed tomography (CT) images used to detect lung cancer, it is difficult to accurately and rapidly diagnose this disease. Doing so requires high expertise by radiologists. Therefore the demand for computer aided diagnosis systems in this area has been increased. The core of all lung cancer detection systems is the distinction between cancer and non-cancerous tissues. The main objective of this study is to present a new method based on 3D convolutional neural networks (CNN) that can perform false positives reduction operations while providing high sensitivity. In...
Automatic Classification of Masses in Mammographic Images using Sparse Representation
, M.Sc. Thesis Sharif University of Technology ; Manzouri, Mohammad Taghi (Supervisor)
Abstract
Computer Aided Diagnosis (CAD) systems are widely used in different medical tasks. Radiology is a branch of medicine which takes advantage of image processing techniques to help radiologists, analyse complicated radiologic images. Among all kind of medical imagingprocedures, utilization of screening mammographyisgetting very popularin detection of breast abnormalities. A typical CAD system for mammogram analysis uses image enhancement and segmentation as pre-processing phase, and feature extraction and classification for detection phase. In this thesis, we have studied different approaches in each level of image processing required in a mammogram mass classification systems, and introduced a...
Radial multiscale cyst segmentation in ultrasound images of kidney
, Article Fourth IEEE International Symposium on Signal processing and Information Technology, ISSPIT 2004, Rome, 18 December 2004 through 21 December 2004 ; 2004 , Pages 42-45 ; 0780386892 (ISBN) ; Kasaei, S ; Jahed, M ; Sharif University of Technology
2004
Abstract
Cysts are one of the most common lesions in kidneys and can be diagnosed exploiting ultrasound images. In this paper, we develop an automatic approach for cyst segmentation in ultrasound images. The proposed approach comprises three steps: finding a seed point in the object exploiting the Gibbs random field, detecting the boundary based on multiresolution signal processing and edge refining, and finally shape model features extracting to verify whether the object is a cyst. The proposed approach is fast and less complex to suit ultrasound exploration applications. © 2004 IEEE
Mammogram image retrieval via sparse representation
, Article 2011 1st Middle East Conference on Biomedical Engineering, MECBME 2011, Sharjah, 21 February 2011 through 24 February 2011 ; 2011 , Pages 63-66 ; 9781424470006 (ISBN) ; Ghaffari, A ; Fatemizadeh, E ; Sharif University of Technology
2011
Abstract
In recent years there has been a great effort to enhance the computer-aided diagnosis systems, since proven similar pathologies, in the past, plays an important role in diagnosis of the current cases, content based medical image retrieval has been emerged. In this work we have designed a decision making machine in which utilizes sparse representation technique to preserve semantic category relevance among the retrieved images and the query image, this machine comprises optimized wavelets (adapted using lifting scheme) to extract appropriate visual features in order to grasp visual content of the images, afterwards by using some classical methods, Raw data vectors become applicable for sparse...
Towards an automatic diagnosis system for lumbar disc herniation: the significance of local subset feature selection
, Article Biomedical Engineering - Applications, Basis and Communications ; 2018 ; 10162372 (ISSN) ; Fayaz, F ; Nikravan, M ; Ahmadi, F ; Dolatabad, M. R ; Sharif University of Technology
World Scientific Publishing Co. Pte Ltd
2018
Abstract
Herniation in the lumbar area is one of the most common diseases which results in lower back pain (LBP) causing discomfort and inconvenience in the patients' daily lives. A computer aided diagnosis (CAD) system can be of immense benefit as it generates diagnostic results within a short time while increasing precision of diagnosis and eliminating human errors. We have proposed a new method for automatic diagnosis of lumbar disc herniation based on clinical MRI data. We use T2-W sagittal and myelograph images. The presented method has been applied on 30 clinical cases, each containing 7 discs (210 lumbar discs) for the herniation diagnosis. We employ Otsu thresholding method to extract the...
An efficient fractal method for detection and diagnosis of breast masses in mammograms
, Article Journal of Digital Imaging ; Vol. 27, issue. 5 , 2014 , pp. 661-669 ; ISSN: 08971889 ; AhmadiNoubari, H ; Fatemizadeh, E ; Khalili, M ; Sharif University of Technology
Abstract
In this paper, we present an efficient fractal method for detection and diagnosis of mass lesion in mammogram which is one of the abnormalities in mammographic images. We used 110 images that were carefully selected by a radiologist, and their abnormalities were also confirmed by biopsy. These images included circumscribed benign, ill-defined, and spiculated malignant masses. Firstly, we discriminated lesions automatically using new fractal dimensions. The results which were examined by different types of breast density showed that the proposed method was able to yield quite satisfactory detection results. Secondly, noting that contours of masses playing the most important role in diagnosis...
Toward a computer aided diagnosis system for lumbar disc herniation disease based on MR images analysis
, Article Biomedical Engineering - Applications, Basis and Communications ; Volume 28, Issue 6 , 2016 ; 10162372 (ISSN) ; Ebrahimzadeh, E ; Izadi, M. R ; Mikaeili, M ; Sharif University of Technology
World Scientific Publishing Co. Pte Ltd
Abstract
Lumbar disc diseases are the commonest complaint of Lower Back Pain (LBP). In this paper, a new method for automatic diagnosis of lumbar disc herniation is proposed which is based on clinical Magnetic Resonance Images (MRI) data. We use T2-W sagittal and myelograph images. Our method uses Otsu thresholding method to extract the spinal cord from MR images of Lumbar disc. In the next step, a third-order polynomial is aligned on the extracted spinal cords, and in the end of preprocessing step all the T2-W sagittal images are prepared for extracting disc boundary and labeling. After labeling and extracting a ROI for each disc, intensity and shape features are used for classification. The...
Coronary artery disease detection using computational intelligence methods
, Article Knowledge-Based Systems ; Volume 109 , 2016 , Pages 187-197 ; 09507051 (ISSN) ; Zangooei, M. H ; Hosseini, M. J ; Habibi, J ; Khosravi, A ; Roshanzamir, M ; Khozeimeh, F ; Sarrafzadegan, N ; Nahavandi, S ; Sharif University of Technology
Elsevier B.V
Abstract
Nowadays, cardiovascular diseases are very common and are one of the main causes of death worldwide. One major type of such diseases is the coronary artery disease (CAD). The best and most accurate method for the diagnosis of CAD is angiography, which has significant complications and costs. Researchers are, therefore, seeking novel modalities for CAD diagnosis via data mining methods. To that end, several algorithms and datasets have been developed. However, a few studies have considered the stenosis of each major coronary artery separately. We attempted to achieve a high rate of accuracy in the diagnosis of the stenosis of each major coronary artery. Analytical methods were used to...
Esophageal gross tumor volume segmentation using a 3D convolutional neural network
, Article Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 16 September 2018 through 20 September 2018 ; Volume 11073 LNCS , 2018 , Pages 343-351 ; 03029743 (ISSN); 9783030009366 (ISBN) ; Sokooti, H ; Elmahdy, M. S ; Peters, F. P ; Manzuri Shalmani, M. T ; Zinkstok, R. T ; Staring, M ; Sharif University of Technology
Springer Verlag
2018
Abstract
Accurate gross tumor volume (GTV) segmentation in esophagus CT images is a critical task in computer aided diagnosis (CAD) systems. However, because of the difficulties raised by the contrast similarity between esophageal GTV and its neighboring tissues in CT scans, this problem has been addressed weakly. In this paper, we present a 3D end-to-end method based on a convolutional neural network (CNN) for this purpose. We leverage design elements from DenseNet in a typical U-shape. The proposed architecture consists of a contractile path and an extending path that includes dense blocks for extracting contextual features and retrieves the lost resolution respectively. Using dense blocks leads to...
A novel digital dentistry platform based on cloud manufacturing paradigm
, Article International Journal of Computer Integrated Manufacturing ; Volume 32, Issue 11 , 2019 , Pages 1024-1042 ; 0951192X (ISSN) ; Fatahi Valilai, O ; Houshmand, M ; Vasegh, Z ; Sharif University of Technology
Taylor and Francis Ltd
2019
Abstract
Dentistry technology has been developed drastically during past two decades and the application of software and automated digital equipment have been proposed to have the most influence. Digital dental workflow includes four phases: scan, design, production planning and production which in each of the phases, the various software packages and digital devices are required. In addition, workflow in dentistry requires intensive cooperation among dentists, dental laboratories, technicians, imaging and production centres. This paper has developed anovel platform for digital dentistry, which facilitates the interoperability among different digital components and improves digital workflow in...
Medical image segmentation for skin lesion detection via topological data analysis
, Article 16th International Conference on Ubiquitous Information Management and Communication, IMCOM 2022, 3 January 2022 through 5 January 2022 ; 2022 ; 9781665426787 (ISBN) ; Jazayeri, F ; Sajedi, H ; Sharif University of Technology
Institute of Electrical and Electronics Engineers Inc
2022
Abstract
According to the WHO, two individuals die every hour from skin cancer and about 9500 people get skin cancer every day just in the United States. Various computer vision algorithms have been introduced for skin lesion detection, classification, and segmentation. This paper proposes a new segmentation-based algorithm in order to select target components using the persistence diagram of the input images. The results, in comparison with the existing seven different both clustering-and histogram-based segmentation methods using three metrics, show improved performance. Medical image segmentation is an essential task in computer-aided diagnosis. The main improvement of our method is to detect one...
Multi-class segmentation of skin lesions via joint dictionary learning
, Article Biomedical Signal Processing and Control ; Volume 68 , 2021 ; 17468094 (ISSN) ; Mahdavi Amiri, N ; Sharif University of Technology
Elsevier Ltd
2021
Abstract
Melanoma is the deadliest type of human skin cancer. However, it is curable if diagnosed in an early stage. Recently, computer aided diagnosis (CAD) systems have drawn much interests. Segmentation is a crucial step of a CAD system. There are different types of skin lesions having high similarities in terms of color, shape, size and appearance. Most available works focus on a binary segmentation. Due to the huge variety of skin lesions and high similarities between different types of lesions, multi-class segmentation is still a challenging task. Here, we propose a method based on joint dictionary learning for multi-class segmentation of dermoscopic images. The key idea is based on combining...
Gradient vector flow snake segmentation of breast lesions in dynamic contrast-enhanced MR images
, Article 2010 17th Iranian Conference of Biomedical Engineering, ICBME 2010 - Proceedings, 3 November 2010 through 4 November 2010, Isfahan ; 2010 ; 9781424474844 (ISBN) ; Fatemizadeh, E ; Gity, M ; Sharif University of Technology
Abstract
The development of computer-aided diagnosis (CAD) for breast magnetic resonance (MR) images has encountered some big challenges. One of these challenges is related to breast lesion segmentation. Accurate segmentation of breast lesions has a vital role in other consequent applications such as feature extraction. Since malignant breast lesions typically appear with irregular borders and shapes in MR images whereas benign masses appear with more regular shapes, and smooth and lobulated borders, it seems that the accurate segmentation of breast lesion borders in MR images are important. To achieve this purpose, we have used the Gradient Vector Flow (GVF) snake segmentation method. This study...
Detecting lung cancer lesions in CT images using 3D convolutional neural networks
, Article 4th International Conference on Pattern Recognition and Image Analysis, IPRIA 2019, 6 March 2019 through 7 March 2019 ; 2019 , Pages 114-118 ; 9781728116211 (ISBN) ; Jamzad, M ; Sharif University of Technology
Institute of Electrical and Electronics Engineers Inc
2019
Abstract
Early diagnosis of lung cancer is very important in improving patients life expectancies. Due to the high number of Computed Tomography (CT) images, fast and accurate diagnosis is difficult for radiologists. Therefore, there is an increasing demand for Computer-Aid Diagnosis (CAD) lung cancer. The core of all lung cancer detection systems is the distinction between cancer and non-cancerous tissues. This operation is performed in the false positive reduction phase, which is one of the most critical part of the lung cancer detection systems. The primary objective of this paper is to present a new method based on 3D Convolutional Neural Networks (CNN) that can reduce the false positives rate...
An intelligent hybrid classification algorithm integrating fuzzy rule-based extraction and harmony search optimization: Medical diagnosis applications
, Article Knowledge-Based Systems ; Volume 220 , 2021 ; 09507051 (ISSN) ; Abdullah, S ; Akhavan Niaki, S. T ; Banihashemi, S ; Sharif University of Technology
Elsevier B.V
2021
Abstract
Uncertainty is a critical factor in medical datasets needed to be overcome for increasing diagnosis efficiency. This paper proposes an intelligent classification algorithm comprising a fuzzy rule-based approach, a harmony search (HS) algorithm, and a heuristic algorithm to classify medical datasets intelligently. Two fuzzy approaches, as well as orthogonal and triangular fuzzy sets, are first utilized to define the attributes of data. Then, an HS algorithm is integrated with a heuristic to generate fuzzy rules to select the best rules in the fuzzy rule-based systems. Moreover, to improve the performance of the proposed classification approach, a three-phase parameter tuning approach is...
Detection of inappropriate working conditions for the timing belt in internal-combustion engines using vibration signals and data mining
, Article Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering ; Volume 231, Issue 3 , 2017 , Pages 418-432 ; 09544070 (ISSN) ; Banakar, A ; Ghobadian, B ; Agha Mirsalim, M ; Minaei, S ; Jafari, S. M ; Sharif University of Technology
SAGE Publications Ltd
2017
Abstract
Abnormal operating conditions for the timing belt can lead to cracks, fatigue, sudden rupture and damage to engines. In this study, an intelligent system was developed to detect and classify high-load operating conditions and high-temperature operating conditions for timing belts. To achieve this, vibration signals in normal operating conditions, high-load operating conditions and high-temperature operating conditions were collected. Time-domain signals were transformed to the frequency domain and the time-frequency domain using the fast Fourier transform method and the wavelet transform method respectively. In the data-mining stage, 25 statistical features were extracted from different...