Computer Aided Detection of Prostate Cancer using Fused Information from Dynamic Contrast Enhanced and Morphological Magnetic Resonance Images

Ampeliotis, Dimitris; Antonakoudi, A.; Berberidis, Kostas; Psarakis, Emmanouil Ζ.

doi:10.1109/icspc.2007.4728462

Cited by 15 publications

(23 citation statements)

References 12 publications

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“…These comparisons are subjective as all metrics are highly influenced by the number of datasets and modalities. For example, the method in [10] produced the lowest sensitivity based on 303 prostates, whereas the method in [12] shows higher accuracy in 10 cases but has not been tested on a larger dataset. The method proposed by Sung et al [2] achieved the best results in all metrics while method proposed by Vos et al [3] achieved similar result in terms of CCR using combination of different modalities (T2-W+DCE).…”

Section: Resultsmentioning

confidence: 82%

“…As a result, the probability of cancerous tissues being missed is higher (false negatives). Therefore, the ultimate goal of our research is to develop Computer Aided Diagnosis (CAD) tools for prostate cancer detection by segmenting area (in our case a sub region) which has the highest probability being malignant, hence could help clinicians to perform targeted biopsies and have the potential to improve the accuracy of clinical diagnostic tests [12]. Figure 1 shows an example of prostate gland with random biopsies which represents the current scenario of biopsy tests.…”

Section: Introductionmentioning

confidence: 98%

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Computer-aided diagnosis method for MRI-guided prostate biopsy within the peripheral zone using grey level histograms

2015

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This paper describes a computer-aided diagnosis method for targeted prostate biopsies within the peripheral zone in T2-Weighted MRI. We subdivided the peripheral zone into four regions and compare each sub region's grey level histogram with malignant and normal histogram models, and use specific metrics to estimate the presence of abnormality. The initial evaluation based on 200 MRI slices taken from 40 different patients and we achieved 87% correct classification rate with 89% and 86% sensitivity and specificity, respectively. The main contribution of this paper is a novel approach of Computer Aided Diagnosis which is using grey level histograms analysis between sub regions. In clinical point of view, the developed method could assist clinicians to perform targeted biopsies which are better than the random ones which are currently used.

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Section: Resultsmentioning

confidence: 82%

Section: Introductionmentioning

confidence: 98%

Computer-aided diagnosis method for MRI-guided prostate biopsy within the peripheral zone using grey level histograms

2015

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“…Sham et al , developed a decision support system that used SVM to generate cancer probability maps from multiparametric MR images, and Niaf et al , reported that SVM produced the best result based on a comparison study of four different supervised learning methods (SVM, linear discriminant analysis, k ‐nearest neighbors, and naive Bayes classifiers) based on a feature set derived from gray‐level images such as first‐order statistics, Haralick features, and gradient features. This was followed by a study in , which is 2% below the best performance (and 1% above the result of our proposed method) in Table . Ampeliotis et al , used probabilistic neural networks to classify a set of feature vectors extracted from T2‐W morphological images and T1‐W DCE.…”

Section: Discussionmentioning

confidence: 86%

“…This was followed by a study in , which is 2% below the best performance (and 1% above the result of our proposed method) in Table . Ampeliotis et al , used probabilistic neural networks to classify a set of feature vectors extracted from T2‐W morphological images and T1‐W DCE. Other methods achieved more than 80% accuracy.…”

Section: Discussionmentioning

confidence: 86%

Computer‐aided diagnosis: detection and localization of prostate cancer within the peripheral zone

Rampun

Chen

Malcolm

et al. 2015

Numer Methods Biomed Eng

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We propose a methodology for prostate cancer detection and localization within the peripheral zone based on combining multiple segmentation techniques. We extract four image features using Gaussian and median filters. Subsequently, we use each image feature separately to generate binary segmentations. Finally, we take the intersection of all four binary segmentations, incorporating a model of the peripheral zone, and perform erosion to remove small false-positive regions. The initial evaluation of this method is based on 275 MRI images from 37 patients, and 86% of the slices were classified correctly with 87% and 86% sensitivity and specificity achieved, respectively. This paper makes two contributions: firstly, a novel computer-aided diagnosis approach, which is based on combining multiple segmentation techniques using only a small number of simple image features, and secondly, the development of the proposed method and its application in prostate cancer detection and localization using a single MRI modality with the results comparable with the state-of-the-art multimodality and advanced computer vision methods in the literature. Copyright © 2015 John Wiley & Sons, Ltd.

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“…Finally, for classification tasks, we observed the use of classifiers such as k-nearest neighbor (Filipczuk et al, 2013;Gedik and Atasoy, 2013;Gopinath and Shanthi, 2013;He et al, 2011;Muramatsu et al, 2013;Nava et al, 2014;Odeh et al, 2006;Osman et al, 2009;Raja et al, 2010;Verikas et al, 2006), artificial neural networks (Barhoumi et al, 2007;Geetha et al, 2008;Jasmine et al, 2009;López et al, 2008;Raja et al, 2007;Streba et al, 2012;Verma, 2009;Wu et al, 2006), Bayesian classifiers (Ampeliotis et al, 2007;Bhooshan et al, 2011;Garnavi et al, 2012;Gruszauskas et al, 2008Gruszauskas et al, , 2009Retter et al, 2013;Tolouee et al, 2011) techniques based on linear discriminant analysis (Lee et al, 2009;Muramatsu et al, 2013;Tanner et al, 2006) and logistic regression models (Shen et al, 2007;Tanner et al, 2006).…”

Section: Tasks For Computer-aided Diagnosis Systemsmentioning

confidence: 99%

A systematic review on the evaluation and characteristics of computer-aided diagnosis systems

Gonçalves

Delamaro

Nunes

2014

Rev. Bras. Eng. Bioméd.

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Introduction: One of the challenges in developing Computer-Aided Diagnosis (CAD) systems is their accurate and comprehensive assessment. This paper presents the conduction and results of a systematic review (SR) that aims to verify the state of the art regarding the assessment of CAD systems. This survey provides a general analysis of the current status of the design, development and assessment of such systems and includes discussions on the most used metrics and approaches that could be utilized to obtain more objective evaluation methods. Methods: The SR was conducted using the scientifi c databases, ACM Digital Library, IEEE Xplore Digital Library, ScienceDirect and Web of Science. Inclusion and exclusion criteria were defi ned and applied to each retrieved work to select those of interest. From 156 studies retrieved, 100 studies were included. Results: There is a number of abnormalities that have been used for the development of CAD systems. Images from computed tomographies and mammographies are the most encountered types of medical images. Additionally, a number of studies used public databases for CAD evaluations. The main evaluation metrics and methods applied to CAD systems include sensitivity, accuracy, specifi city and receiver operating characteristic (ROC) analyses. In the assessed CAD systems that used the segmentation method, 30.0% applied the overlap measure. Discussion: There remain several topics to explore for the assessment of CAD schemes. While some evaluation metrics are traditionally used, they require a prior knowledge of case characteristics to test CAD systems. We were not able to identify articles that use software testing to evaluate CAD systems. Thus, we realize that there is a gap between CAD assessments and traditional practices of software engineering. However, the scope of this research is limited to scientifi c and academic works and excludes commercial interests. Finally, we discuss potential research studies within this scope to create a more objective and effi cient evaluation of CAD systems.

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Computer Aided Detection of Prostate Cancer using Fused Information from Dynamic Contrast Enhanced and Morphological Magnetic Resonance Images

Cited by 15 publications

References 12 publications

Computer-aided diagnosis method for MRI-guided prostate biopsy within the peripheral zone using grey level histograms

Computer-aided diagnosis method for MRI-guided prostate biopsy within the peripheral zone using grey level histograms

Computer‐aided diagnosis: detection and localization of prostate cancer within the peripheral zone

A systematic review on the evaluation and characteristics of computer-aided diagnosis systems

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