Computer-aided detection of prostate cancer

Llobet, Rafael; Pérez-Cortés, Juan-Carlos; Toselli, Alejandro H.; Juan, Alfons

doi:10.1016/j.ijmedinf.2006.03.001

Cited by 55 publications

(32 citation statements)

References 18 publications

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“…We implemented all the methods of the studies of Huynen et al, 2 Llobet et al, 6 and Yfantes et al 4 However, the method of Llobet et al 6 did not produce a meaningful result for our database. In Llobet et al 6 , the ROC curve using their method can be found. Table 3 gives a comparison of our methods with the previous methods.…”

Section: Resultsmentioning

confidence: 99%

“…However, DRE easily misses small tumors, and PSA values are dependent on several factors that are not caused only by prostate cancer. 6 Several imaging types can be used to make a more reliable diagnosis, and transrectal ultrasound (TRUS) is one of them.…”

Section: Introductionmentioning

confidence: 99%

“…Values of 90% sensitivity and 64% specificity were obtained. Llobet et al 6 also used co-occurrence matrices, known as the gray level dependence matrix, for texture features. They applied two classifiers, k-nearest neighborhood and Hidden Markov models, to classify the pixels and they compared both results.…”

Section: Introductionmentioning

confidence: 99%

“…However, discrimination between benign and cancerous tissues based only on texture features is extremely difficult due to the problem of imperfect supervision (incorrect labeling of specific pixels). 6 Pixels cannot be labeled as positive or negative by simply analyzing the images at the texture level. Therefore, it is necessary to analyze other features of cancerous regions as well as discriminative texture features.…”

Section: Introductionmentioning

confidence: 99%

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Computer-aided Prostate Cancer Detection using Texture Features and Clinical Features in Ultrasound Image

2008

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In this paper, we propose a new prostate detection method using multiresolution autocorrelation texture features and clinical features such as location and shape of tumor. With the proposed method, we can detect cancerous tissues efficiently with high specificity (about 90-95%)and high sensitivity (about 92-96%) by the measurement of the number of correctly classified pixels. Multiresolution autocorrelation can detect cancerous tissues efficiently, and clinical knowledge helps to discriminate the cancer region by location and shape of the region and increases specificity. The support vector machine is used to classify tissues based on those features. The proposed method will be helpful in formulating a more reliable diagnosis, increasing diagnosis efficiency.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Computer-aided Prostate Cancer Detection using Texture Features and Clinical Features in Ultrasound Image

2008

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“…From the pixel/voxel intensities, several features can be obtained. For example, Gaussian statistics (mean and standard deviation) of pixel/voxels intensities are used as features in several TRUS studies [151][152][153][154][155]. The Nakagami distribution has also been used for extracting features from pixel intensities in various studies [153,154].…”

Section: Trus Feature Extraction and Diagnosismentioning

confidence: 99%

A novel NMF-based DWI CAD framework for prostate cancer.

McClure¹

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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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Computer-aided detection of prostate cancer

Cited by 55 publications

References 18 publications

Computer-aided Prostate Cancer Detection using Texture Features and Clinical Features in Ultrasound Image

Computer-aided Prostate Cancer Detection using Texture Features and Clinical Features in Ultrasound Image

A novel NMF-based DWI CAD framework for prostate cancer.

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

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