Extraction of Canine Cataract Object for Developing Handy Pre-diagnostic Tool with Fuzzy Stretching and ART2 Learning

Kim, Kwang-Baek

doi:10.5391/ijfis.2016.16.1.21

Cited by 5 publications

(6 citation statements)

References 6 publications

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“…In this paper, we apply fuzzy stretching for that purpose like we used in [6] but change the membership function from the usual triangle type to trapezoid type that has been successful in many applications where the intensity distribution seems to be irregular [13].…”

Section: Brightness Enhancement With Fuzzy Stretchingmentioning

confidence: 99%

“…This effort has been done by our team for many years with different technical algorithms involved [6], [11], [12].…”

Section: Dynamic Controlled Fuzzy C-means In Quantizationmentioning

confidence: 99%

“…However, it needs pet breeder's continuous care and knowledge about the pet's status. As noted in [6], there have been several researches using ultrasonography [7], MRI [8], endoscopic evaluation technique [9] and, if we can extract characteristic features for the diagnosis, even machine learning techniques might be applicable [10] to assist the medical expert.…”

Section: Introductionmentioning

confidence: 99%

“…We have tried various image processing algorithms in conjunction with some artificial intelligent techniques such as ART2 learning, and Fuzzy C-Means (FCM) image to overcome given noise prone cellular phone image analysis [6], [11], [12].…”

Section: Introductionmentioning

confidence: 99%

“…When we applied ART2 [6], the software can not discriminate white hair around eyes from cataract in ART2 clustering process thus there was an inaccurate cataract extraction case. FCM was considered as the alternative of ART2 to overcome that problem.…”

Section: Introductionmentioning

confidence: 99%

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Intelligent Automatic Extraction of Canine Cataract Object with Dynamic Controlled Fuzzy C-Means based Quantization

Kim¹,

Song²

2018

IJECE

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Canine cataract is developed with aging and can cause the blindness or surgical treatment if not treated timely. Since the pet owner do not have professional knowledge nor professional equipment, there is a growing need of providing pre-diagnosis software that can extract cataract-suspicious regions from simple photographs taken by cellular phones for the sake of preventive public health. In this paper, we propose a software that is highly successful for that purpose. The proposed software uses dynamic control of FCM clusters in quantification and trapezoid membership function in fuzzy stretching in order to enhance the intensity contrast from such rough photograph input. Through experiment, the proposed system demonstrates sufficiently enough accuracy in extraction (successful in 42 out of 45 cases) with better quality comparing with previous attempt.

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Section: Brightness Enhancement With Fuzzy Stretchingmentioning

confidence: 99%

“…This effort has been done by our team for many years with different technical algorithms involved [6], [11], [12].…”

Section: Dynamic Controlled Fuzzy C-means In Quantizationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Intelligent Automatic Extraction of Canine Cataract Object with Dynamic Controlled Fuzzy C-Means based Quantization

Kim¹,

Song²

2018

IJECE

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AI-driven canine cataract detection: a machine learning approach using support vector machine

Jones,

Vijayan

2024

Journal of the Chinese Institute of Engineers

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Pet dog disease pre-diagnosis system for caregiver with possibilistic C-means clustering and disease database

Kim

Song

2020

IJEECS

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<p>While the population of pet dogs and veterinary clinics are increasing, there is no reliable and useful software for pet owners/caregivers who have limited knowledge on the pet diseases. In this paper, we propose a pre-diagnosis system working on the mobile platform that the pet owner can take a pre-diagnosis from his/her observation of pet dog’s abnormality. Technically, the system needs a reliable databases for disease-symptom association thus we provide it based on the textbook and encyclopedia. Then, we apply Possibilistic C-Means algorithm that is an unsupervised machine learning algorithm to form the connections between disease and symptoms from database. The system outputs five most probable diseases from the observed symptoms of pet dog. The utility of this system is to alert the owner’s attention on the pet dog’s abnormal behavior and try to find the diseases as soon as possible.</p>

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Extraction of Canine Cataract Object for Developing Handy Pre-diagnostic Tool with Fuzzy Stretching and ART2 Learning

Cited by 5 publications

References 6 publications

Intelligent Automatic Extraction of Canine Cataract Object with Dynamic Controlled Fuzzy C-Means based Quantization

Intelligent Automatic Extraction of Canine Cataract Object with Dynamic Controlled Fuzzy C-Means based Quantization

AI-driven canine cataract detection: a machine learning approach using support vector machine

Pet dog disease pre-diagnosis system for caregiver with possibilistic C-means clustering and disease database

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