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Chubu University, Japan

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23 Papers

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

How New Information Criteria WAIC and WBIC Worked for MLP Model Selection

Topics: Neural Networks; Model Selection; Regression

Authors: Seiya Satoh ¹ and Ryohei Nakano ²

Affiliations: ¹ National Institute of Advanced Industrial Science and Tech, Japan , ² Chubu University, Japan

Digital Object Identifier: 10.5220/0006120301050111

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

Quick Abstract

The present paper evaluates newly invented information criteria for singular models. Well-known criteria such as AIC and BIC are valid for regular statistical models, but their validness for singular models is not guaranteed. Statistical models such as multilayer perceptrons (MLPs), RBFs, HMMs are singular models. Recently WAIC and WBIC have been proposed as new information criteria for singular models. They are developed on a strict mathematical basis, and need empirical evaluation. This paper experimentally evaluates how WAIC and WBIC work for MLP model selection using conventional and new learning methods.

Related Ontology Subjects/Areas/Topics (18)

Artificial Intelligence	Methodologies and Methods	Physiological Computing Systems
Biomedical Engineering	Model Selection	Regression
Biomedical Signal Processing	Neural Networks	Sensor Networks
Computational Intelligence	Neurocomputing	Signal Processing
Health Engineering and Technology Applications	Neurotechnology, Electronics and Informatics	Soft Computing
Human-Computer Interaction	Pattern Recognition	Theory and Methods

Performance of Complex-Valued Multilayer Perceptrons Largely Depends on Learning Methods

Topic: Learning Paradigms and Algorithms

Authors: Seiya Satoh ¹ and Ryohei Nakano ²

Affiliations: ¹ National Institute of Advanced Industrial Science and Tech, Japan , ² Chubu University, Japan

Digital Object Identifier: 10.5220/0006496500450053

Proceedings of IJCCI 2017/IJCCI, ISBN: 978-989-758-274-5

Quick Abstract

Complex-valued multilayer perceptrons (C-MLPs) can naturally treat complex numbers, and therefore can work well for the processing of signals such as radio waves and sound waves, which are naturally expressed as complex numbers. The performance of C-MLPs can be measured by solution quality and processing time. We believe the performance seriously depends on which learning methods we employ since in the search space there exist many local minima and singular regions, which prevent learning methods from finding excellent solutions. Complex-valued backpropagation (C-BP) and complex-valued BFGS method (C-BFGS) are well-known for learning C-MLPs. Moreover, complex-valued singularity stairs following (C-SSF) has recently been proposed as a new learning method, which achieves successive learning by utilizing singular regions and guarantees monotonic decrease of training errors. Through experiments using five datasets, this paper evaluates how the performance of C-MLPs changes depending on learning methods.

Related Ontology Subjects/Areas/Topics (17)

Artificial Intelligence	Learning Paradigms and Algorithms	Physiological Computing Systems
Biomedical Engineering	Methodologies and Methods	Sensor Networks
Biomedical Signal Processing	Neural Networks	Signal Processing
Computational Intelligence	Neurocomputing	Soft Computing
Health Engineering and Technology Applications	Neurotechnology, Electronics and Informatics	Theory and Methods
Human-Computer Interaction	Pattern Recognition

Design of a Low-false-positive Gesture for a Wearable Device

Topics: Classification; Learning of Action Patterns

Authors: Ryo Kawahata ¹ , Atsushi Shimada ¹ , Takayoshi Yamashita ² , Hideaki Uchiyama ¹ and Rin-ichiro Taniguchi ¹

Affiliations: ¹ Kyushu University, Japan , ² Chubu University, Japan

Digital Object Identifier: 10.5220/0005701905810588

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

Quick Abstract

As smartwatches are becoming more widely used in society, gesture recognition, as an important aspect of interaction with smartwatches, is attracting attention. An accelerometer that is incorporated in a device is often used to recognize gestures. However, a gesture is often detected falsely when a similar pattern of action occurs in daily life. In this paper, we present a novel method of designing a new gesture that reduces false detection. We refer to such a gesture as a low-false-positive (LFP) gesture. The proposed method enables a gesture design system to suggest LFP motion gestures automatically. The user of the system can design LFP gestures more easily and quickly than what has been possible in previous work. Our method combines primitive gestures to create an LFP gesture. The combination of primitive gestures is recognized quickly and accurately by a random forest algorithm using our method. We experimentally demonstrate the good recognition performance of our method for a designed gesture with a high recognition rate and without false detection.

Related Ontology Subjects/Areas/Topics (6)

Applications		Learning of Action Patterns		Software Engineering
Classification		Pattern Recognition		Theory and Methods

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

Guided Filtering using Reflected IR Image for Improving Quality of Depth Image

Topics: Image Enhancement and Restoration; Image Generation Pipeline: Algorithms and Techniques

Authors: Takahiro Hasegawa , Ryoji Tomizawa , Yuji Yamauchi , Takayoshi Yamashita and Hironobu Fujiyoshi

Affiliation: Chubu University, Japan

Digital Object Identifier: 10.5220/0005717800330039

Proceedings of VISAPP 2016/VISIGRAPP, ISBN: 978-989-758-175-5

Quick Abstract

We propose the use of a reflected IR image as a guide image to improve the quality of depth image. Guided filtering is a technique that can quickly remove noise from a depth image by using a guide image. However, when an RGB image is used as a guide image, the quality of depth image does not be improved if the RGB image contains texture information (such as surface patterns and shadows). In this study, our aim is to obtain a depth image of higher quality by using a guide image derived from a reflected IR image, which have less texture information and a high correlation with depth image. Using reflected IR image, it is possible to perform filtering while retaining edge information between objects of different materials, without being affected by textures on the surfaces of these objects. In evaluation experiments, we confirmed that a guide image based on reflected IR image produce better denoising effects than RGB guide image. From the results of upsampling tests, we also confirmed that the proposed IR based guided filtering has a higher PSNR than that of RGB image.

Related Ontology Subjects/Areas/Topics (4)

Computer Vision, Visualization and Computer Graphics		Image Formation and Preprocessing		Image Generation Pipeline: Algorithms and Techniques
Image Enhancement and Restoration

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