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Morphological Techniques for Medical Images: A Review  [cached]
Isma Irum,Mudassar Raza,Muhammad Sharif
Research Journal of Applied Sciences, Engineering and Technology , 2012,
Abstract: Image processing is playing a very important role in medical imaging with its versatile applications and features towards the development of computer aided diagnostic systems, automatic detections of abnormalities and enhancement in ultrasonic, computed tomography, magnetic resonance images and lots more applications. Medical images morphology is a field of study where the medical images are observed and processed on basis of geometrical and changing structures. Medical images morphological techniques has been reviewed in this study underlying the some human organ images, the associated diseases and processing techniques to address some anatomical problem detection. Images of Human brain, bone, heart, carotid, iris, lesion, liver and lung have been discussed in this study.
Computer Aided Interpretation Approach for Optical Tomographic Images  [PDF]
Christian D. Klose,Alexander D. Klose,Uwe Netz,Juergen Beuthan,Andreas H. Hielscher
Physics , 2010, DOI: 10.1117/1.3516705
Abstract: A computer-aided interpretation approach is proposed to detect rheumatic arthritis (RA) of human finger joints in optical tomographic images. The image interpretation method employs a multi-variate signal detection analysis aided by a machine learning classification algorithm, called Self-Organizing Mapping (SOM). Unlike in previous studies, this allows for combining multiple physical image parameters, such as minimum and maximum values of the absorption coefficient for identifying affected and not affected joints. Classification performances obtained by the proposed method were evaluated in terms of sensitivity, specificity, Youden index, and mutual information. Different methods (i.e., clinical diagnostics, ultrasound imaging, magnet resonance imaging and inspection of optical tomographic images), were used as "ground truth"-benchmarks to determine the performance of image interpretations. Using data from 100 finger joints, findings suggest that some parameter combinations lead to higher sensitivities while others to higher specificities when compared to single parameter classifications employed in previous studies. Maximum performances were reached when combining minimum/maximum-ratio and image variance with respect to ultra sound as benchmark. In this case, sensitivity and specificity of 0.94 and 0.96 respectively were achieved. These values are much higher than results reported when a) other classification techniques were applied or b) single parameter classifications were used, where sensitivities and specificities of 0.71 were achieved.
Automatic Localization of the Left Ventricle from Cardiac Cine Magnetic Resonance Imaging: A New Spectrum-Based Computer-Aided Tool  [PDF]
Liang Zhong, Jun-Mei Zhang, Xiaodan Zhao, Ru San Tan, Min Wan
PLOS ONE , 2014, DOI: 10.1371/journal.pone.0092382
Abstract: Traditionally, cardiac image analysis is done manually. Automatic image processing can help with the repetitive tasks, and also deal with huge amounts of data, a task which would be humanly tedious. This study aims to develop a spectrum-based computer-aided tool to locate the left ventricle using images obtained via cardiac magnetic resonance imaging. Discrete Fourier Transform was conducted pixelwise on the image sequence. Harmonic images of all frequencies were analyzed visually and quantitatively to determine different patterns of the left and right ventricles on spectrum. The first and fifth harmonic images were selected to perform an anisotropic weighted circle Hough detection. This tool was then tested in ten volunteers. Our tool was able to locate the left ventricle in all cases and had a significantly higher cropping ratio of 0.165 than did earlier studies. In conclusion, a new spectrum-based computer aided tool has been proposed and developed for automatic left ventricle localization. The development of this technique, which will enable the automatic location and further segmentation of the left ventricle, will have a significant impact in research and in diagnostic settings. We envisage that this automated method could be used by radiographers and cardiologists to diagnose and assess ventricular function in patients with diverse heart diseases.
Alzheimer’s Disease Detection in Brain Magnetic Resonance Images Using Multiscale Fractal Analysis  [PDF]
Salim Lahmiri,Mounir Boukadoum
ISRN Radiology , 2013, DOI: 10.5402/2013/627303
Abstract: We present a new automated system for the detection of brain magnetic resonance images (MRI) affected by Alzheimer’s disease (AD). The MRI is analyzed by means of multiscale analysis (MSA) to obtain its fractals at six different scales. The extracted fractals are used as features to differentiate healthy brain MRI from those of AD by a support vector machine (SVM) classifier. The result of classifying 93 brain MRIs consisting of 51 images of healthy brains and 42 of brains affected by AD, using leave-one-out cross-validation method, yielded classification accuracy, 100% sensitivity, and specificity. These results and a processing time of 5.64 seconds indicate that the proposed approach may be an efficient diagnostic aid for radiologists in the screening for AD. 1. Introduction Alzheimer’s disease (AD) is a progressive and degenerative disease that affects brain cells, and its early diagnosis has been essential for appropriate intervention by health professionals. Noninvasive in vivo neuroimaging techniques such as magnetic resonance imaging (MRI) and positron emission tomography (PET) are commonly used to diagnose and monitor the progression of the disease and the effect of treatment. In this regard, the problem of developing computer aided diagnosis (CAD) tools to distinguish images with AD from those of normal brains has been extensively addressed in the past years [1–12]. A review of a recent work follows. Magnin et al. [1] used the relative weight of gray matter versus white matter and cerebrospinal fluid in 90 regions of interests (ROI) as features classified with SVM. Based on the bootstrap method, the SVM obtained 94.5% average classification accuracy in the classification of 16 AD and 22 control (healthy) subjects, with a mean specificity of 96.6% and a mean sensitivity of 91.5%. Ramírez et al. [2] proposed a classification system for AD based on the partial least square (PLS) regression model for feature extraction (identification of discriminative voxels) and the random forest (RF) classifier. The PLS-RF system yielded accuracy, sensitivity, and specificity values of 96.9%, 100%, and 92.7%, respectively, after classifying 41 normal and 56 AD images using the leave-one-out cross-validation method. Salas-Gonzalez et al. [3] used Welch’s -test to identify voxels that provide higher difference between normal and AD images. The identified voxels formed the main features to classify, and a SVM with linear kernel reached 96.2% accuracy in distinguishing 41 normal and 38 AD images using leave-one-out cross-validation method; the sensitivity and the
Hybrid Discrete Wavelet Transform and Gabor Filter Banks Processing for Features Extraction from Biomedical Images  [PDF]
Salim Lahmiri,Mounir Boukadoum
Journal of Medical Engineering , 2013, DOI: 10.1155/2013/104684
Abstract: A new methodology for automatic feature extraction from biomedical images and subsequent classification is presented. The approach exploits the spatial orientation of high-frequency textural features of the processed image as determined by a two-step process. First, the two-dimensional discrete wavelet transform (DWT) is applied to obtain the HH high-frequency subband image. Then, a Gabor filter bank is applied to the latter at different frequencies and spatial orientations to obtain new Gabor-filtered image whose entropy and uniformity are computed. Finally, the obtained statistics are fed to a support vector machine (SVM) binary classifier. The approach was validated on mammograms, retina, and brain magnetic resonance (MR) images. The obtained classification accuracies show better performance in comparison to common approaches that use only the DWT or Gabor filter banks for feature extraction. 1. Introduction Computer-aided diagnosis (CAD) has been the subject of a lot of research as a tool to help health professionals in medical decision making. As a result, many CAD systems integrate image processing, computer vision, and intelligent and statistical machine learning methods to aid radiologists in the interpretation of medical images and ultimately help improve diagnostic accuracy. These systems have been employed to analyze and classify various types of digitized biomedical images, including retina [1, 2], mammograms [3–5], brain magnetic resonance images [6–8], skin cancer images [9, 10], lung images [11, 12], and ulcer detection in endoscopy images [13, 14], just to name a few. The typical CAD process starts with a segmentation stage to identify one or more regions of interest (ROI) in the image of interest. Then, the ROI(s) is processed for image enhancement and/or feature extraction before classification. Because the segmentation step requires prior knowledge of discriminant image features and its implementation typically calls for numerous parameter settings, recent works have attempted to eliminate it. These approaches realize feature space reduction by applying one or more transforms to the whole image and extracting the feature vector to classify from one or more of the obtained components [3, 5, 7–14]. Texture analysis has played an important role in the characterization of biomedical images. Texture analysis methods can be categorized as statistical, geometrical, and signal processing types [14]. Statistical methods are mainly based on the spatial distribution of pixel gray values, while geometrical approaches depend on the geometric
Computer-aided assessment of diagnostic images for epidemiological research
Alison G Abraham, Donald D Duncan, Stephen J Gange, Sheila West
BMC Medical Research Methodology , 2009, DOI: 10.1186/1471-2288-9-74
Abstract: Using the example of cortical cataract detection, we developed an algorithm for assisting a reviewer in evaluating digital images for the presence and severity of lesions. Available image processing and statistical methods that were easily implementable were used as the basis for the CAD algorithm. The performance of the system was compared to the subjective assessment of five reviewers using 60 simulated images. Cortical cataract severity scores from 0 to 16 were assigned to the images by the reviewers and the CAD system, with each image assessed twice to obtain a measure of variability. Image characteristics that affected reviewer bias were also assessed by systematically varying the appearance of the simulated images.The algorithm yielded severity scores with smaller bias on images where cataract severity was mild to moderate (approximately ≤ 6/16ths). On high severity images, the bias of the CAD system exceeded that of the reviewers. The variability of the CAD system was zero on repeated images but ranged from 0.48 to 1.22 for the reviewers. The direction and magnitude of the bias exhibited by the reviewers was a function of the number of cataract opacities, the shape and the contrast of the lesions in the simulated images.CAD systems are feasible to implement with available software and can be valuable when medical images contain exposure or outcome information for etiologic research. Our results indicate that such systems have the potential to decrease bias and discriminate very small changes in disease severity. Simulated images are a tool that can be used to assess performance of a CAD system when a gold standard is not available.Diagnostics are becoming increasingly image based. Whether the setting is clinical practice or research, information must be extracted from an image to determine disease status. The determination of the presence or severity of disease will impact clinical care for a patient or outcome status in a study. In many clinical arenas image
A Computer Aided Detection system for mammographic images implemented on a GRID infrastructure  [PDF]
U. Bottigli,P. Cerello,P. Delogu,M. E. Fantacci,F. Fauci,G. Forni,B. Golosio,P. L. Indovina,A. Lauria,E. Lopez Torres,R. Magro,G. L. Masala,P. Oliva,R. Palmiero,G. Raso,A. Retico,A. Stefanini,S. Stumbo,S. Tangaro
Physics , 2003,
Abstract: The use of an automatic system for the analysis of mammographic images has proven to be very useful to radiologists in the investigation of breast cancer, especially in the framework of mammographic-screening programs. A breast neoplasia is often marked by the presence of microcalcification clusters and massive lesions in the mammogram: hence the need for tools able to recognize such lesions at an early stage. In the framework of the GPCALMA (GRID Platform for Computer Assisted Library for MAmmography) project, the co-working of italian physicists and radiologists built a large distributed database of digitized mammographic images (about 5500 images corresponding to 1650 patients) and developed a CAD (Computer Aided Detection) system, able to make an automatic search of massive lesions and microcalcification clusters. The CAD is implemented in the GPCALMA integrated station, which can be used also for digitization, as archive and to perform statistical analyses. Some GPCALMA integrated stations have already been implemented and are currently on clinical trial in some italian hospitals. The emerging GRID technology can been used to connect the GPCALMA integrated stations operating in different medical centers. The GRID approach will support an effective tele- and co-working between radiologists, cancer specialists and epidemiology experts by allowing remote image analysis and interactive online diagnosis.
Computer-aided differential diagnosis system for Alzheimer’s disease based on machine learning with functional and morphological image features in magnetic resonance imaging  [PDF]
Yasuo Yamashita, Hidetaka Arimura, Takashi Yoshiura, Chiaki Tokunaga, Ohara Tomoyuki, Koji Kobayashi, Yasuhiko Nakamura, Nobuyoshi Ohya, Hiroshi Honda, Fukai Toyofuku
Journal of Biomedical Science and Engineering (JBiSE) , 2013, DOI: 10.4236/jbise.2013.611137

Alzheimer’s disease (AD) is a dementing disorder and one of the major public health problems in countries with greater longevity. The cerebral cortical thickness and cerebral blood flow (CBF), which are considered as morphological and functional image features, respectively, could be decreased in specific cerebral regions of patients with dementia of Alzheimer type. Therefore, the aim of this study was to develop a computer-aided classification system for AD patients based on machine learning with the morphological and functional image features derived from a magnetic resonance (MR) imaging system. The cortical thicknesses in ten cerebral regions were derived as morphological features by using gradient vector trajectories in fuzzy membership images. Functional CBF maps were measured with an arterial spin labeling technique, and ten regional CBF values were obtained by registration between the CBF map and Talairach atlas using an affine transformation and a free form deformation. We applied two systems based on an arterial neural network (ANN) and a support vector machine (SVM), which were trained with 4 morphological and 6 functional image features, to 15 AD patients and 15 clinically normal (CN) subjects for classification of AD. The area under the receiver operating characteristic curve (AUC) values for the two systems based on the ANN and SVM with both image features were 0.901 and 0.915, respectively. The AUC values for the ANN-and SVM-based systems with the morphological features were 0.710 and 0.660, respectively, and those with the functional features were 0.878 and 0.903, respectively. Our preliminary results suggest that the proposed method may have potential for assisting radiologists in the differential diagnosis of AD patients by using morphological and functional image features.

Computer- Aided diagnosis system for the evaluation of chronic obstructive pulmonary disease on CT Images
Parsa Hosseini M,Soltanian-Zadeh H,Akhlaghpoor Sh
Tehran University Medical Journal , 2011,
Abstract: "n Normal 0 false false false EN-US X-NONE AR-SA MicrosoftInternetExplorer4 /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-qformat:yes; mso-style-parent:""; mso-padding-alt:0cm 5.4pt 0cm 5.4pt; mso-para-margin:0cm; mso-para-margin-bottom:.0001pt; mso-pagination:widow-orphan; font-size:11.0pt; font-family:"Calibri","sans-serif"; mso-ascii-font-family:Calibri; mso-ascii-theme-font:minor-latin; mso-fareast-font-family:"Times New Roman"; mso-fareast-theme-font:minor-fareast; mso-hansi-font-family:Calibri; mso-hansi-theme-font:minor-latin; mso-bidi-font-family:Arial; mso-bidi-theme-font:minor-bidi;} Background: Chronic Obstructive Pulmonary Disease (COPD) is one of the most prevalent pulmonary diseases. Use of an automatic system for the detection and diagnosis of the disease will be beneficial to the patients' treatment decision-making process. In this paper, we propose a new approach for the Computer Aided Diagnosis (CAD) of the disease and determination of its severity axial CT scan images."n"nMethods: In this study, 24 lung CT scans in full inspiratory and expiratory states were performed. Variations in the normalized pattern of the lungs' external parenchyma were exploited as a feature for COPD diagnosis. Subsequently, a Bayesian classifier was used to classify variations into two normal and abnormal patterns for the discrimination of patients and healthy individuals. Finally, the accuracy of the classification was assessed statistically. "n"nResults: With the proposed method, the lungs parenchymal elasticity and air-trapping were determined quantitatively. The more this feature tended to zero, the more severe air-trapping and obstructive pulmonary disease is. By analyzing CT images in the healthy and patient groups, we calculated the hard threshold for the diagnosis of the disease. Clinical results tested by the mentioned method, suggested the effectiveness of this approach."n"nConclusion: In regard to the challenges of COPD diagnosis, we propose a new computer-aided design which may be helpful to physicians for a more accurate diagnosis of the disease. Moreover, this severity scoring algorithm may be useful for targeted disease management and risk-adjustment.
Differentiation of Pancreatic Cancer and Chronic Pancreatitis Using Computer-Aided Diagnosis of Endoscopic Ultrasound (EUS) Images: A Diagnostic Test  [PDF]
Maoling Zhu, Can Xu, Jianguo Yu, Yijun Wu, Chunguang Li, Minmin Zhang, Zhendong Jin, Zhaoshen Li
PLOS ONE , 2013, DOI: 10.1371/journal.pone.0063820
Abstract: Background Differentiating pancreatic cancer (PC) from normal tissue by computer-aided diagnosis of EUS images were quite useful. The current study was designed to investigate the feasibility of using computer-aided diagnostic (CAD) techniques to extract EUS image parameters for the differential diagnosis of PC and chronic pancreatitis (CP). Methodology/Principal Findings This study recruited 262 patients with PC and 126 patients with CP. Typical EUS images were selected from the sample sets. Texture features were extracted from the region of interest using computer-based techniques. Then the distance between class algorithm and sequential forward selection (SFS) algorithm were used for a better combination of features; and, later, a support vector machine (SVM) predictive model was built, trained, and validated. Overall, 105 features of 9 categories were extracted from the EUS images for pattern classification. Of these features, the 16 were selected as a better combination of features. Then, SVM predictive model was built and trained. The total cases were randomly divided into a training set and a testing set. The training set was used to train the SVM, and the testing set was used to evaluate the performance of the SVM. After 200 trials of randomised experiments, the average accuracy, sensitivity, specificity, the positive and negative predictive values of pancreatic cancer were 94.2±0.1749%,96.25±0.4460%, 93.38±0.2076%, 92.21±0.4249% and 96.68±0.1471%, respectively. Conclusions/Significance Digital image processing and computer-aided EUS image differentiation technologies are highly accurate and non-invasive. This technology provides a kind of new and valuable diagnostic tool for the clinical determination of PC.
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