Computer-Aided Diagnosis Systems for Brain Diseases in Magnetic Resonance Images

Arimura, Hidetaka; Magome, Taiki; Yamashita, Yasuo; Yamamoto, Daisuke

doi:10.3390/a2030925

Cited by 73 publications

(38 citation statements)

References 93 publications

(138 reference statements)

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“…Other techniques that can be considered for the preprocessing stage are: enhancement filter [70,71], contrast limited adaptive histogram equalization [72],auto-enhancement [73], wiener filter [73], fast Fourier transform [74], wavelet transform [74], anti-geometric diffusion [74], erosion filter [75], smoothing filters [76] and noise correction [76]. Figure 1 gives an illustration of preprocessing applied to a lung CT image, in which an improved distribution of level colors and, consequently, a better visualization of the lung and its structures, is accomplished.…”

Section: Preprocessingmentioning

confidence: 99%

Automatic 3D pulmonary nodule detection in CT images: A survey

Valente

Cortez

Neto

et al. 2016

Computer Methods and Programs in Biomedicine

204

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This work presents a systematic review of techniques for the 3D automatic detection of pulmonary nodules in computerized-tomography (CT) images.Its main goals are to analyze the latest technology being used for the development of computational diagnostic tools to assist in the acquisition, storage and, mainly, processing and analysis of the biomedical data. Also, this work identifies the progress made, so far, evaluates the challenges to be overcome and provides an analysis of future prospects. As far as the authors know, this is the first time that a review is devoted exclusively to automated 3D techniques for the detection of pulmonary nodules from lung CT images, which makes this work of noteworthy value. The research covered the published works in the Web of Science, PubMed, Science Direct and IEEEXplore up to December 2014. Each work found that referred to automated 3D segmentation of the lungs was individually analyzed to identify its objective, methodology and results. Based on the analysis of the selected works, several studies were seen to be useful for the construction of medical diagnostic * Corresponding author. E-mail: tavares@fe.up.pt. 31, 2015 aid tools. However, there are certain aspects that still require attention such as increasing algorithm sensitivity, reducing the number of false positives, improving and optimizing the algorithm detection of different kinds of nodules with different sizes and shapes and, finally, the ability to integrate with the Electronic Medical Record Systems and Picture Archiving and Communication Systems. Based on this analysis, we can say that further research is needed to develop current techniques and that new algorithms are needed to overcome the identified drawbacks. Preprint submitted to Computer Methods and Programs in Biomedicine August

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Section: Preprocessingmentioning

confidence: 99%

Automatic 3D pulmonary nodule detection in CT images: A survey

Valente

Cortez

Neto

et al. 2016

Computer Methods and Programs in Biomedicine

204

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“…4. The CAD system is cost-effective and efficient and can be used as a decision support system by the experts in the diagnosis and treatment of neurological disorders [6]. Section 2 of this paper provides brief information about current medical technologies in the neurological disease diagnosis and also discusses challenges in medical big data analysis.…”

Section: Introductionmentioning

confidence: 99%

“…The algorithms of major CAD systems are developed by using techniques and theories of the pattern recognition field, and thus the CAD is involved as one of the pattern recognition fields [6]. The techniques of the CAD systems consist of data pre-processing, feature extraction and classification as discussed in Sect.…”

Section: Introductionmentioning

confidence: 99%

Medical Big Data: Neurological Diseases Diagnosis Through Medical Data Analysis

2016

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“…Many authors have proposed various algorithms for classification of brain tumors by developing various computer aided diagnostic (CAD) system based on different features and classification algorithm [1][2][3][4]. Generally, these systems use image processing techniques such as feature extraction, selection, and classification.…”

Section: Background Theorymentioning

confidence: 99%

Computer Aided Diagnosis System-A Decision Support System for Clinical Diagnosis of Brain Tumours

Tiwari¹,

Sachdeva²,

Ahuja³

et al. 2017

IJCIS

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The iso, hypo or hyper intensity, similarity of shape, size and location complicates the identification of brain tumors. Therefore, an adequate Computer Aided Diagnosis (CAD) system is designed for classification of brain tumor for assisting inexperience radiologists in diagnosis process. A multifarious database of real post contrast T1-weighted MR images from 10 patients has been taken. This database consists of primary brain tumors namely Meningioma (MENI-class 1), Astrocytoma (AST-class 2), and Normal brain regions (NORM-class 3). The region of interest(s) (ROIs) of size 20 x 20 is extracted by the radiologists from each image in the database. A total of 371 texture and intensity features are extracted from these ROI(s). An Artificial Neural Network (ANN) is used to classify these three classes as it shows better classification results on multivariate non-linear, complicated, rule based domains, and decision making domains. It is being observed that ANN provides much accurate results in terms of individual classification accuracy and overall classification accuracy. The four discrete experiments have been performed. Initially, the experiment was performed by extracting 263 features and an overall classification accuracy 78.10% is achieved, however, it was noticed that MENI (class-1) was highly misclassified with AST (class-2). Further, to improve the overall classification accuracy and individual classification accuracy specifically for MENI (class-1), LAWs textural energy measures (LTEM) are added in the feature bank (263+108=371). An individual class accuracy of 91.40% is obtained for MENI (class-1), 91.43% for AST (class-2), 94.29% for NORM (class-3) and an overall classification accuracy of 92.43% is achieved. The results are calculated with and without addition of LTEM feature with Principle component analysis (PCA)-ANN. LTEM-PCA-ANN approach improved results with an overall accuracy of 93.34%. The texture patterns obtained were clear enough to differentiate between MENI (class-1) and AST (class-2) despite of necrotic and cystic component and location and size of tumor. LTEM detected fundamental texture properties such as level, edge, spot, wave and ripple in both horizontal and vertical directions which boosted the texture energy.

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Computer-Aided Diagnosis Systems for Brain Diseases in Magnetic Resonance Images

Cited by 73 publications

References 93 publications

Automatic 3D pulmonary nodule detection in CT images: A survey

Automatic 3D pulmonary nodule detection in CT images: A survey

Medical Big Data: Neurological Diseases Diagnosis Through Medical Data Analysis

Computer Aided Diagnosis System-A Decision Support System for Clinical Diagnosis of Brain Tumours

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