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Aichi Medical University, Japan

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10 Papers

PVT based Blood Vessel Segmentation and Polyp Size Estimation in Colonoscopy Images

Topics: Medical Imaging; Deep Learning and Neural Networks; Classification and Clustering; Image and Video Analysis and Understanding

Authors: Insaf Setitra ¹ , Yuji Iwahori ² , Yacine Elhamer ¹ , Anais Mezrag ¹ , Shinji Fukui ³ and Kunio Kasugai ⁴

Affiliations: ¹ Department of Artificial Intelligence and Data Science, University of Science and Technology Houari Bouemediene, USTHB, Algiers, Algeria , ² Department of Computer Science, Chubu University, Kasugai, Aichi, 487-8501 Japan , ³ Department of Information Education, Aichi University of Education, Kariya, Aichi, 448-0001 Japan , ⁴ Department of Gastroenterology, Aichi Medical University, Nagakute, Aichi, 480-1195 Japan

Digital Object Identifier: 10.5220/0011666700003411

Proceedings of ICPRAM 2023, ISBN: 978-989-758-626-2

Quick Abstract

The size of colorectal polyps is one of the factors conditioning the risk of synchronous and metachronous colorectal cancer (CRC). In this work, we are interested in the automatic measurement of polyp sizes in colonoscopy videos. The study is performed in two steps: (1) first the detection and segmentation of the polyp by the neural network Polyp-PVT and then (2) the classification of the polyp into different classes (type of disease, size of the polyp). This is done by extracting information from blood vessels, a parameter that has a low variability and is present in the majority of colonoscopic videos. This method has been validated by two local Hepato-Gastro-Enterology specialists. Once the size of the polyp is extracted, a classification of polyps as susceptible malignant (polyp size ≥ 6 mm) and susceptible benign (polyp size < 6 mm) is performed. Our approach reaches an accuracy of 85.61% for the first category and 73.92% for the second one and is comparable to human classification which is estimated to 52% for beginners and 71% for experts endoscopists.

Polyp Classification and Clustering from Endoscopic Images using Competitive and Convolutional Neural Networks

Topics: Medical Imaging; Deep Learning and Neural Networks; Image and Video Analysis and Understanding

Authors: Avish Kabra ¹ , Yuji Iwahori ² , Hiroyasu Usami ² , M. K. Bhuyan ¹ , Naotaka Ogasawara ³ and Kunio Kasugai ³

Affiliations: ¹ Indian Institute of Technology Guwahati, Assam, 781039 and India , ² Chubu University, 487-8501 and Japan , ³ Aichi Medical University, 1-1 Yazakokarimata, Nagakute, Aichi, 480-1195 and Japan

Digital Object Identifier: 10.5220/0007353204460452

Proceedings of ICPRAM 2019, ISBN: 978-989-758-351-3

Quick Abstract

Understanding the type of Polyp present in the body plays an important role in medical diagnosis. This paper proposes an approach to classify and cluster the polyp present in an Endoscopic scene into malignant or benign class. CNN and Self Organizing Maps are used to classify and cluster from white light and Narrow Band (NBI) Endoscopic Images . Using Competitive Neural Network different polyps available from previous data are plotted with the new polyp according to their structural similarity. Such kind of presentation not only help the doctor in it’s easy understanding but also helps him to know what kind of medical procedures were followed in similar cases.

Related Ontology Subjects/Areas/Topics (4)

Applications		Pattern Recognition		Software Engineering
Medical Imaging

Polyp Shape Recovery using Vascular Border from Single Colonoscopy Image

Topics: Medical Imaging and Diagnosis; Image Processing Methods; Feature Recognition and Extraction Methods

Authors: Hiroyasu Usami ¹ , Yuji Iwahori ¹ , M. K. Bhuyan ² , Aili Wang ³ , Naotaka Ogasawara ⁴ and Kunio Kasugai ⁴

Affiliations: ¹ Department of Computer Science, Chubu University, Kasugai, Japan , ² Department of Electronics and Electrical Engineering, Indian Institute of Technology Guwahati, Guwahati, India , ³ Higher Education Key Lab., Harbin University of Science and Technology, Harbin, China , ⁴ Department of Gastroenterology, Aichi Medical University, Nagakute, Japan

Digital Object Identifier: 10.5220/0007379101040111

Proceedings of BIOIMAGING 2019/BIOSTEC, ISBN: 978-989-758-353-7

Quick Abstract

The shape and size of a colonic polyp is a biomarker that correlates with its risk of malignancy and guides its clinical management. It is the most accurate method for detecting polyps of all sizes, and it allows biopsy of lesions and resection of most polyps, and it is considered nowadays as the gold standard for colon screening. However, there are still open challenges to overcome, such as the reduction of the missing rate. Colonoscopy images usually consist of nonrigid objects such as a polyp, and no approaches have been proposed to recovery shape and absolute size from a single image. Hence, it is a challenging topic to reconstruct polyp shape using computer vision technique. This paper proposes a polyp shape retrieval method based on Shape from Shading (SFS), and this research contributes to mitigating constraint for applying SFS to the single colonoscopy image using vascular border information. Experiments confirmed that the proposed method recovered approximate polyp shapes.

Dense 3D Reconstruction of Endoscopic Polyp

Topics: Medical Imaging and Diagnosis; Image Processing Methods; Image Fusion Methods

Authors: Ankur Deka ¹ , Yuji Iwahori ² , M. K. Bhuyan ¹ , Pradipta Sasmal ¹ and Kunio Kasugai ³

Affiliations: ¹ Indian Institute of Technology Guwahati, India , ² Chubu University, Japan , ³ Aichi Medical University, Japan

Digital Object Identifier: 10.5220/0006720701590166

Proceedings of BIOIMAGING 2018/BIOSTEC, ISBN: 978-989-758-278-3

Quick Abstract

This paper proposes a model for 3D reconstruction of polyp in endoscopic scene. 3D shape of polyp enables better understanding of the medical condition and can help predict abnormalities like cancer. While there has been significant progress in monocular shape recovery, the same hasn’t been the case with endoscopic images due to challenges like specular regions. We take advantage of the advances in shape recovery and suitably apply these with modifications to the scenario of endoscopic images. The model operates on 2 nearby video frames. ORB features are detected and tracked for computing camera motion and initial rough depth estimation. This is followed by a dense pixelwise operation which gives a dense depth map of the scene. Our method shows positive results and strong correspondence with the ground truth.

A Robust Method for Blood Vessel Extraction in Endoscopic Images with SVM-based Scene Classification

Topics: Bioinformatics and Systems Biology; Image Understanding; Medical Imaging

Authors: Mayank Golhar ¹ , Yuji Iwahori ² , M. K. Bhuyan ¹ , Kenji Funahashi ³ and Kunio Kasugai ⁴

Affiliations: ¹ Indian Institute of Technology Guwahati, India , ² Chubu University, Japan , ³ Nagoya Insutitute of Technology, Japan , ⁴ Aichi Medical University, Japan

Digital Object Identifier: 10.5220/0006192601480156

Proceedings of ICPRAM 2017, ISBN: 978-989-758-222-6

Quick Abstract

This paper proposes a model for blood vessel detection in endoscopic images. A novel SVM-based scene classification of endoscopic images is used. This SVM-based model classifies images into four classes on the basis of dye content and blood vessel presence in the scene, using various colour, edge and texture based features. After classification, a vessel extraction method is proposed which is based on the Frangi vesselness approach. In original Frangi Vesselness results, it is observed that many non-blood vessel edges are inaccurately detected as blood vessels. So, two additions are proposed, background subtraction and a novel dissimilarity-detecting filtering procedure, which are able to discriminate between blood vessel and non-blood vessel edges by exploiting the symmetric nature property of blood vessels. It was found that the proposed approach gave better accuracy of blood vessel extraction when compared with the vanilla Frangi Vesselness approach and BCOSFIRE filter, another state-of-art vessel delineation approach.

Related Ontology Subjects/Areas/Topics (7)

Applications	Image Understanding	Pattern Recognition
Bioinformatics and Systems Biology	Medical Imaging	Software Engineering
Computer Vision, Visualization and Computer Graphics

Automatic Polyp Detection from Endoscope Image using Likelihood Map based on Edge Information

Topics: Bioinformatics and Systems Biology; Image Understanding; Object Recognition; Medical Imaging

Authors: Yuji Iwahori ¹ , Hiroaki Hagi ¹ , Hiroyasu Usami ¹ , Robert J. Woodham ² , Aili Wang ³ , M. K. Bhuyan ⁴ and Kunio Kasugai ⁵

Affiliations: ¹ Chubu University, Japan , ² University of British Columbia, Canada , ³ Harbin University of Science and Technology, China , ⁴ Indian Institute of Technology Guwahati, India , ⁵ Aichi Medical University, Japan

Digital Object Identifier: 10.5220/0006189704020409

Proceedings of ICPRAM 2017, ISBN: 978-989-758-222-6

Quick Abstract

An endoscope is a medical instrument that acquires images inside the human body. This paper proposes a new approach for the automatic detection of polyp regions in an endoscope image by generating a likelihood map with both of edge and color information to obtain high accuracy so that probability becomes high at around polyp candidate region. Next, Histograms of Oriented Gradients (HOG) features are extracted from the detected region and random forests are applied for the classification to judge whether the detected region is polyp region or not. It is shown that the proposed approach has high accuracy for the polyp detection and the usefulness is confirmed through the computer experiments with endoscope images.

Related Ontology Subjects/Areas/Topics (8)

Applications	Image Understanding	Pattern Recognition
Bioinformatics and Systems Biology	Medical Imaging	Software Engineering
Computer Vision, Visualization and Computer Graphics	Object Recognition

Estimating Reflectance Parameter of Polyp using Medical Suture Information in Endoscope Image

Topics: Sensors and Early Vision; Medical Imaging

Authors: Yuji Iwahori ¹ , Daiki Yamaguchi ² , Tsuyoshi Nakamura ² , Boonserm Kijsirikul ³ , M. K. Bhuyan ⁴ and Kunio Kasugai ⁵

Affiliations: ¹ Chubu University, Japan , ² Nagoya Institute of Technology, Japan , ³ Chulalongkorn University, Thailand , ⁴ Indian Institute of Technology Guwahati, India , ⁵ Aichi Medical University, Japan

Digital Object Identifier: 10.5220/0005649305030509

Proceedings of ICPRAM 2016, ISBN: 978-989-758-173-1

Quick Abstract

An endoscope is a medical instrument that acquires images inside the human body. In this paper, a new 3-D reconstruction approach is proposed to estimate the size and shape of the polyp under conditions of both point light source illumination and perspective projection. Previous approaches could not know the size of polyp without assuming reflectance parameters as known constant. Even if it was possible to estimate the absolute size of polyp, it was assumed that the parameter of camera movement ∆Z is treated as a known along the depth direction. Here two images are used with a medical suture which is known size object to solve this problem and the proposed approach shows the parameter of camera movement can be estimated with robust accuracy with correspondence between two images taken via slight movement of Z. Experiments with endoscope images are demonstrated to evaluate the validity of proposed approach.

Related Ontology Subjects/Areas/Topics (5)

Applications		Pattern Recognition		Software Engineering
Medical Imaging		Sensors and Early Vision

Improvement of Recovering Shape from Endoscope Images Using RBF Neural Network

Topics: Sensors and Early Vision; Shape Representation; Medical Imaging

Authors: Yuji Iwahori ¹ , Seiya Tsuda ¹ , Robert J. Woodham ² , M. K. Bhuyan ³ and Kunio Kasugai ⁴

Affiliations: ¹ Chubu University, Japan , ² University of British Columbia, Canada , ³ Indian Institute of Technology Guwahati, India , ⁴ Aichi Medical University, Japan

Digital Object Identifier: 10.5220/0005206800620070

Proceedings of ICPRAM 2015, ISBN: 978-989-758-077-2

Quick Abstract

The VBW (Vogel-Breuß-Weickert) model is proposed as a method to recover 3-D shape under point light source illumination and perspective projection. However, the VBW model recovers relative, not absolute, shape. Here, shape modification is introduced to recover the exact shape. Modification is applied to the output of the VBW model. First, a local brightest point is used to estimate the reflectance parameter from two images obtained with movement of the endoscope camera in depth. After the reflectance parameter is estimated, a sphere image is generated and used for Radial Basis Function Neural Network (RBF-NN) learning. The NN implements the shape modification. NN input is the gradient parameters produced by the VBW model for the generated sphere. NN output is the true gradient parameters for the true values of the generated sphere. Depth can then be recovered using the modified gradient parameters. Performance of the proposed approach is confirmed via computer simulation and real experiment.

Related Ontology Subjects/Areas/Topics (6)

Applications		Pattern Recognition		Shape Representation
Medical Imaging		Sensors and Early Vision		Software Engineering

Automatic Polyp Detection using DSC Edge Detector and HOG Features

Topics: Image Understanding; Object Recognition; Medical Imaging

Authors: Himanshu Agrahari ¹ , Yuji Iwahori ² , M. K. Bhuyan ¹ , Somnath Ghorai ¹ , Himanshu Kohli ¹ , Robert J. Woodham ³ and Kunio Kasugai ⁴

Affiliations: ¹ Indian Institute of Guwahati, India , ² Chubu University, Japan , ³ University of British Columbia, Canada , ⁴ Aichi Medical University, Japan

Digital Object Identifier: 10.5220/0004756104950501

Proceedings of ICPRAM 2014, ISBN: 978-989-758-018-5

Quick Abstract

Endoscopy is a very powerful technology to examine the intestinal tract and to detect the presence of any possible abnormalities like polyps, the main cause of cancer. This paper presents an edge based method for polyp detection in endoscopic video images. It utilizes discrete singular convolution (DSC) algorithm for edge detection/segmentation scheme, then by using conic fitting techniques (ellipse and hyperbola) potential candidates are determined. These candidates are first rotated so as to make major axis in the x-axis direction, and then classified as polyp or non-polyp by SVM classifier which is trained separately for ellipse and hyperbola with HOG features.

Related Ontology Subjects/Areas/Topics (7)

Applications	Medical Imaging	Pattern Recognition
Computer Vision, Visualization and Computer Graphics	Object Recognition	Software Engineering
Image Understanding

EFFECTIVENESS FOR A SLEEPINESS TEST OF PUPIL SIZE ESTIMATION DURING BLINK

Topics: Medical Signal Acquisition, Analysis and Processing; Time and Frequency Response; Physiological Processes and Bio-Signal Modeling, Non-Linear Dynamics

Authors: Minoru Nakayama ¹ , Keiko Yamamoto ² and Fumio Kobayashi ²

Affiliations: ¹ CRADLE (The Center for Research and Development of Educational Technolgy), Tokyo Institute of Technology, Japan , ² Aichi Medical University, Japan

Digital Object Identifier: 10.5220/0001058105580564

Proceedings of BIOSIGNALS 2008/BIOSTEC, ISBN: 978-989-8111-18-0

Quick Abstract

Pupillary response has been used for an index of sleepiness, but the validity of the index is not clear. In this paper, the influence of blinks on the Pupillary Unrest Index (PUI) and the Power Spectrum Density (PSD) for the frequency range 0.01 < f < 0.8H z, as indices of pupil’s instability during a sleepiness test, was examined. To estimate pupil size during blink, a procedure for collecting the clinical data was developed using Support Vector Regression (SVR). The values of PUI increased with experimental time, and the values and deviations of PUI for experimental observation were larger than the ones with SVR estimation. The blink time also increased with experimental time, and there were significant correlation relationships between the value of PUI and blink time. The mean PSD also correlated significantly with blink time. The relationship between pupillary indices and a subjective sleepiness index was not significant, as it was not in other previous works. These results provide evidence that pupillary indices were significantly affected by blink, and they did not reflect sleepiness correctly.

Related Ontology Subjects/Areas/Topics (10)

Applications and Services	Informatics in Control, Automation and Robotics	Signal Processing, Sensors, Systems Modeling and Control
Biomedical Engineering	Medical Image Detection, Acquisition, Analysis and Processing	Time and Frequency Response
Biomedical Signal Processing	Physiological Processes and Bio-Signal Modeling, Non-Linear Dynamics	Time-Frequency Analysis
Computer Vision, Visualization and Computer Graphics

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