Literature DB >> 8197622

Analysis of ultrasonographic prostate images for the detection of prostatic carcinoma: the automated urologic diagnostic expert system.

A L Huynen1, R J Giesen, J J de la Rosette, R G Aarnink, F M Debruyne, H Wijkstra.   

Abstract

This paper describes a study on the automated analysis of ultrasonographic prostate images. With image processing, tissue characterization in the prostate was performed to assess the probability of malignancy. During prostate examinations, images were recorded at the positions where biopsies were taken. The used samples were divided into three groups. Two of them were used for the construction of a classification tree, and the third was used for the evaluation of this classification. A sensitivity of 80.6% and specificity of 77.1% were reached retrospectively. In a prospective way, these results were 80.0% and 88.2%, respectively. The prospective predictive value for cancer detection was 85.7%. The presented prospective value for image analysis was almost twice as high as the values normally found for prostate examination.

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Year:  1994        PMID: 8197622     DOI: 10.1016/0301-5629(94)90011-6

Source DB:  PubMed          Journal:  Ultrasound Med Biol        ISSN: 0301-5629            Impact factor:   2.998


  8 in total

1.  [Transrectal ultrasound of the prostate. Current status and prospects].

Authors:  M Zacharias; K V Jenderka; H Heynemann; P Fornara
Journal:  Urologe A       Date:  2002-11       Impact factor: 0.639

2.  ΤND: a thyroid nodule detection system for analysis of ultrasound images and videos.

Authors:  Eystratios G Keramidas; Dimitris Maroulis; Dimitris K Iakovidis
Journal:  J Med Syst       Date:  2010-09-14       Impact factor: 4.460

3.  Construction and application of hierarchical decision tree for classification of ultrasonographic prostate images.

Authors:  R J Giesen; A L Huynen; R G Aarnink; J J de la Rosette; F M Debruyne; H Wijkstra
Journal:  Med Biol Eng Comput       Date:  1996-03       Impact factor: 2.602

4.  Haralick texture analysis of prostate MRI: utility for differentiating non-cancerous prostate from prostate cancer and differentiating prostate cancers with different Gleason scores.

Authors:  Andreas Wibmer; Hedvig Hricak; Tatsuo Gondo; Kazuhiro Matsumoto; Harini Veeraraghavan; Duc Fehr; Junting Zheng; Debra Goldman; Chaya Moskowitz; Samson W Fine; Victor E Reuter; James Eastham; Evis Sala; Hebert Alberto Vargas
Journal:  Eur Radiol       Date:  2015-05-21       Impact factor: 5.315

5.  A Weak and Semi-supervised Segmentation Method for Prostate Cancer in TRUS Images.

Authors:  Seokmin Han; Sung Il Hwang; Hak Jong Lee
Journal:  J Digit Imaging       Date:  2020-08       Impact factor: 4.056

6.  Computer-aided prostate cancer detection using texture features and clinical features in ultrasound image.

Authors:  Seok Min Han; Hak Jong Lee; Jin Young Choi
Journal:  J Digit Imaging       Date:  2008-03-06       Impact factor: 4.056

7.  Real-time Burn Classification using Ultrasound Imaging.

Authors:  Sangrock Lee; Hanglin Ye; Deepak Chittajallu; Uwe Kruger; Tatiana Boyko; James K Lukan; Andinet Enquobahrie; Jack Norfleet; Suvranu De
Journal:  Sci Rep       Date:  2020-04-02       Impact factor: 4.379

8.  Texture Feature-Based Classification on Transrectal Ultrasound Image for Prostatic Cancer Detection.

Authors:  Xiaofu Huang; Ming Chen; Peizhong Liu; Yongzhao Du
Journal:  Comput Math Methods Med       Date:  2020-10-06       Impact factor: 2.238

  8 in total

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