Literature DB >> 11204851

Texture analysis for classification of cervix lesions.

Q Ji1, J Engel, E Craine.   

Abstract

This paper presents a generalized statistical texture analysis technique for characterizing and recognizing typical, diagnostically most important, vascular patterns relating to cervical lesions from colposcopic images. The contributions of the research include: 1) the introduction of a generalized texture analysis technique based on the combination of the conventional statistical and structural textural analysis approaches by using a statistical description of geometric primitives; 2) the introduction of a set of textural measures that capture the specific characteristics of cervical textures as perceived by human. Experimental study with real images demonstrated the feasibility and promising of the proposed approach in discriminating between cervical texture patterns indicative of different stages of cervical lesions.

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Year:  2000        PMID: 11204851     DOI: 10.1109/42.896790

Source DB:  PubMed          Journal:  IEEE Trans Med Imaging        ISSN: 0278-0062            Impact factor:   10.048


  11 in total

1.  Andriod Device-Based Cervical Cancer Screening for Resource-Poor Settings.

Authors:  Vidya Kudva; Keerthana Prasad; Shyamala Guruvare
Journal:  J Digit Imaging       Date:  2018-10       Impact factor: 4.056

2.  Automated segmentation algorithm for detection of changes in vaginal epithelial morphology using optical coherence tomography.

Authors:  Shahab Chitchian; Kathleen L Vincent; Gracie Vargas; Massoud Motamedi
Journal:  J Biomed Opt       Date:  2012-11       Impact factor: 3.170

3.  Hybrid Transfer Learning for Classification of Uterine Cervix Images for Cervical Cancer Screening.

Authors:  Vidya Kudva; Keerthana Prasad; Shyamala Guruvare
Journal:  J Digit Imaging       Date:  2020-06       Impact factor: 4.056

4.  Three-dimensional texture analysis of renal cell carcinoma cell nuclei for computerized automatic grading.

Authors:  T Y Kim; H J Choi; H G Hwang; H K Choi
Journal:  J Med Syst       Date:  2009-04-08       Impact factor: 4.460

5.  Volumetric texture features from higher-order images for diagnosis of colon lesions via CT colonography.

Authors:  Bowen Song; Guopeng Zhang; Hongbing Lu; Huafeng Wang; Wei Zhu; Perry J Pickhardt; Zhengrong Liang
Journal:  Int J Comput Assist Radiol Surg       Date:  2014-04-03       Impact factor: 2.924

6.  Extended morphological processing: a practical method for automatic spot detection of biological markers from microscopic images.

Authors:  Yoshitaka Kimori; Norio Baba; Nobuhiro Morone
Journal:  BMC Bioinformatics       Date:  2010-07-08       Impact factor: 3.169

7.  Texture classification by texton: statistical versus binary.

Authors:  Zhenhua Guo; Zhongcheng Zhang; Xiu Li; Qin Li; Jane You
Journal:  PLoS One       Date:  2014-02-10       Impact factor: 3.240

8.  DCE-MRI Texture Features for Early Prediction of Breast Cancer Therapy Response.

Authors:  Guillaume Thibault; Alina Tudorica; Aneela Afzal; Stephen Y-C Chui; Arpana Naik; Megan L Troxell; Kathleen A Kemmer; Karen Y Oh; Nicole Roy; Neda Jafarian; Megan L Holtorf; Wei Huang; Xubo Song
Journal:  Tomography       Date:  2017-03

9.  The first step toward diagnosing female genital schistosomiasis by computer image analysis.

Authors:  Sigve Dhondup Holmen; Elisabeth Kleppa; Kristine Lillebø; Pavitra Pillay; Lisette van Lieshout; Myra Taylor; Fritz Albregtsen; Birgitte Jyding Vennervald; Mathias Onsrud; Eyrun Floerecke Kjetland
Journal:  Am J Trop Med Hyg       Date:  2015-04-27       Impact factor: 2.345

10.  Classification of cervical neoplasms on colposcopic photography using deep learning.

Authors:  Bum-Joo Cho; Youn Jin Choi; Myung-Je Lee; Ju Han Kim; Ga-Hyun Son; Sung-Ho Park; Hong-Bae Kim; Yeon-Ji Joo; Hye-Yon Cho; Min Sun Kyung; Young-Han Park; Byung Soo Kang; Soo Young Hur; Sanha Lee; Sung Taek Park
Journal:  Sci Rep       Date:  2020-08-12       Impact factor: 4.379

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