Abstract
In this paper, we propose a vision based navigation system to guide an endoscope inside human colon. The target to pursue is a dark spot on colonoscopic images, called “pattern”. A novel methodology for "pattern" extraction and tracking was designed. Surgeons observations leads to the basic idea of this technique. Information about target position is then continues and makes possible prediction of the “pattern” position. A set of endoscopic images is tested to demonstrate the effectiveness of the vision technique. An experiment tool to simulate the endoscope navigation was employed to achieve real time performance. An interpretation of the results and the possible amelioration is presented.
Keywords
Source Code and Data
No source code files available for this publication.
Reviews
John Melonakos
Tuesday 22 August 2006
Summary: [Short description of the paper. In two or three phrases describe the problem that was addressed by the authors and the approach they took to solve it.] This paper presents an algorithm for colonoscopic navigation. The algorithm specifically seeks to address the dynamical nature of the colon due to the anatomical contraction and expanding of the colon. The authors present results on one dataset and point out limitations to their method.
Hypothesis: [If Applicable: Describe the assumptions that the authors have made and they hypothesis of their work, note that not all papers will fit the model of hypothesis driven work, for example, the description of an image database, or the description of a toolkit will not be driven by an hypothesis, in which case, please simply write : âNon Applicableâ in this field or delete the subtitle.] Not Applicable
Evidence: [Describe the evidence that the authors provide in order to support their claims in the paper. This is a key component on Open Science, opinions that are not supported by evidence should be labeled as âspeculationsâ or âauthorâs opinionâ while. The same rule applies to the text of the reviews: claims should be supported by evidence] The authors show preliminary results on one dataset.
Open Science: [Describe how much the paper and its addendums adhere to the concept of Open Science. Do the authors provide the source code of the programs used in their experiments? Do the authors provide the input images that they used? Or are those images publicly available? Do the authors provide the output images that they show in the paper? Do the authors provide enough details for you to be able to replicate their work?] No source code or data is provided.
Reproducibility: [Did you reproduce the authorsâ work? Did you download their code? Did you compile it? Did you run it? Did you managed to get the same results that they reported? Were there information missing from the paper, that was necessary for you to reproduce the work? Suggest improvements that will make easier for future readers to reproduce this work.] I was unable to reproduce the results due to lack of source code and data.
Use of Open Source Software: [Did the authors use Open Source software in their work? Do they describe their experience with it, advantages and disadvantages? Do they provide advice for future users of those Open Source packages?] As far as I can tell, the authors make no mention of the platform on which they developed their code.
Open Source Contributions: [Do the authorâs provide their source code? Is it in a form that is usable? Do they describe clearly how to use of the code? How long did it take you to use that code?] No source code was provided.
Code Quality: [If the authors provided their source code: Was the code easy to read? Did they use a modern coding style? Did they rely on non-portable mechanism? Was it suitable for multiple-platforms?] Not Applicable
Applicability to other problems: [Do you find that the authors methods can be applied to other image analysis problems? Suggest other disciplines or even other specific projects that could take advantage of this work] This work is highly optimized for endoscopic navigation and thus may apply to any problems relating to endoscopic navigation.
Suggestions for future work: [Suggest to authors future directions for improving their methods, or other domains from which they could learn technique that could help them advance in their research.] The authors may wish to look to control theory results for some inspiration (perhaps the Kalman filter could be used)?
Requests for additional information from authors: [Did you find that information was missing from the paper? Maybe parameters for running the tests? Maybe some images were missing? Would you like to get more details on how the diagrams, or plots were generated?] Data and source code are needed.
Additional Comments: [This is a free-form field] This appears to be nice work for the guidance of endoscopes. The paper nicely formulates the problem and motivates the research. The paper also details the limitations of the proposed method and includes details for future work.
The review score is based on the Insight Journal reviewer guidelines. See: http://insightsoftwareconsortium.org/wiki/index.php/IJ-Reviewer-Guidelines
Eigil Samset
Wednesday 23 August 2006
Summary: A system for colonoscopy navigation is presented. A computer vision based method based on pattern extraction and tracking was described.
Hypothesis: Non applicable
Evidence: The method was tested one image, and some illustrations are given in the paper. Quantitative validation is missing
Open Science: No source code is provided. The software systems used are not described.
Reproducibility: No source code and no image data was provided
Use of Open Source Software: Not described
Open Source Contributions: No source code provided
Code Quality: No source code provided
Applicability to other problems: The method might be applicable to other endoscopic procedures.