Literature DB >> 17329146

Informative frame classification for endoscopy video.

JungHwan Oh1, Sae Hwang, JeongKyu Lee, Wallapak Tavanapong, Johnny Wong, Piet C de Groen.   

Abstract

Advances in video technology allow inspection, diagnosis and treatment of the inside of the human body without or with very small scars. Flexible endoscopes are used to inspect the esophagus, stomach, small bowel, colon, and airways, whereas rigid endoscopes are used for a variety of minimal invasive surgeries (i.e., laparoscopy, arthroscopy, endoscopic neurosurgery). These endoscopes come in various sizes, but all have a tiny video camera at the tip. During an endoscopic procedure, the tiny video camera generates a video signal of the interior of the human organ, which is displayed on a monitor for real-time analysis by the physician. However, many out-of-focus frames are present in endoscopy videos because current endoscopes are equipped with a single, wide-angle lens that cannot be focused. We need to distinguish the out-of-focus frames from the in-focus frames to utilize the information of the out-of-focus and/or the in-focus frames for further automatic or semi-automatic computer-aided diagnosis (CAD). This classification can reduce the number of images to be viewed by a physician and to be analyzed by a CAD system. We call an out-of-focus frame a non-informative frame and an in-focus frame an informative frame. The out-of-focus frames have characteristics that are different from those of in-focus frames. In this paper, we propose two new techniques (edge-based and clustering-based) to classify video frames into two classes, informative and non-informative frames. However, because intensive specular reflections reduce the accuracy of the classification we also propose a specular reflection detection technique, and use the detected specular reflection information to increase the accuracy of informative frame classification. Our experimental studies indicate that precision, sensitivity, specificity, and accuracy for the specular reflection detection technique and the two informative frame classification techniques are greater than 90% and 95%, respectively.

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Year:  2007        PMID: 17329146     DOI: 10.1016/j.media.2006.10.003

Source DB:  PubMed          Journal:  Med Image Anal        ISSN: 1361-8415            Impact factor:   8.545


  10 in total

1.  Glottal Gap tracking by a continuous background modeling using inpainting.

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Journal:  Med Biol Eng Comput       Date:  2017-05-27       Impact factor: 2.602

2.  A robust method to track colonoscopy videos with non-informative images.

Authors:  Jianfei Liu; Kalpathi R Subramanian; Terry S Yoo
Journal:  Int J Comput Assist Radiol Surg       Date:  2013-02-03       Impact factor: 2.924

3.  Shot boundary detection in endoscopic surgery videos using a variational Bayesian framework.

Authors:  Constantinos Loukas; Nikolaos Nikiteas; Dimitrios Schizas; Evangelos Georgiou
Journal:  Int J Comput Assist Radiol Surg       Date:  2016-06-11       Impact factor: 2.924

4.  A validated subjective rating of display quality: the Maryland Visual Comfort Scale.

Authors:  F Jacob Seagull; Erica Sutton; Tommy Lee; Carlos Godinez; Gyusung Lee; Adrian Park
Journal:  Surg Endosc       Date:  2010-07-30       Impact factor: 4.584

5.  Efficient Bronchoscopic Video Summarization.

Authors:  Patrick D Byrnes; William Evan Higgins
Journal:  IEEE Trans Biomed Eng       Date:  2018-07-24       Impact factor: 4.538

6.  Algorithm for video summarization of bronchoscopy procedures.

Authors:  Mikołaj I Leszczuk; Mariusz Duplaga
Journal:  Biomed Eng Online       Date:  2011-12-20       Impact factor: 2.819

7.  An Ensemble-Based Deep Convolutional Neural Network for Computer-Aided Polyps Identification From Colonoscopy.

Authors:  Pallabi Sharma; Bunil Kumar Balabantaray; Kangkana Bora; Saurav Mallik; Kunio Kasugai; Zhongming Zhao
Journal:  Front Genet       Date:  2022-04-26       Impact factor: 4.772

8.  Closed contour specular reflection segmentation in laparoscopic images.

Authors:  Jan Marek Marcinczak; Rolf-Rainer Grigat
Journal:  Int J Biomed Imaging       Date:  2013-08-01

9.  Wireless Capsule Endoscopy in Correlation with Software Application in Gastrointestinal Diseases.

Authors:  A F Constantinescu; M Ionescu; I Rogoveanu; M E Ciurea; C T Streba; V F Iovanescu; C C Vere
Journal:  Curr Health Sci J       Date:  2015-04-10

10.  Machine Learning-Based Classification of the Health State of Mice Colon in Cancer Study from Confocal Laser Endomicroscopy.

Authors:  Pejman Rasti; Christian Wolf; Hugo Dorez; Raphael Sablong; Driffa Moussata; Salma Samiei; David Rousseau
Journal:  Sci Rep       Date:  2019-12-27       Impact factor: 4.379

  10 in total

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