An automatic diagnosis method for the knee meniscus tears in MR images

Köse, Cemal; Gençalioğlu, Okyay; Şevik, Uğur

doi:10.1016/j.eswa.2007.11.036

Cited by 37 publications

(22 citation statements)

References 24 publications

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“…Thus, the accuracy of the diagnosis increases, and the computation time is reduced as compared to the histogram-based methods. Köse et al [14] proposed a statistical segmentation method to segment the meniscal regions. This method employs the statistical properties of a texture and then segments the bones of the knee.…”

Section: Tear Diagnosismentioning

confidence: 99%

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A Computer-Aided Type-II Fuzzy Image Processing for Diagnosis of Meniscus Tear

et al. 2016

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Meniscal tear is one of the prevalent knee disorders among young athletes and the aging population, and requires correct diagnosis and surgical intervention, if necessary. Not only the errors followed by human intervention but also the obstacles of manual meniscal tear detection highlight the need for automatic detection techniques. This paper presents a type-2 fuzzy expert system for meniscal tear diagnosis using PD magnetic resonance images (MRI). The scheme of the proposed type-2 fuzzy image processing model is composed of three distinct modules: Pre-processing, Segmentation, and Classification. λ-nhancement algorithm is used to perform the pre-processing step. For the segmentation step, first, Interval Type-2 Fuzzy C-Means (IT2FCM) is applied to the images, outputs of which are then employed by Interval Type-2 Possibilistic C-Means (IT2PCM) to perform post-processes. Second stage concludes with re-estimation of "η" value to enhance IT2PCM. Finally, a Perceptron neural network with two hidden layers is used for Classification stage. The results of the proposed type-2 expert system have been compared with a well-known segmentation algorithm, approving the superiority of the proposed system in meniscal tear recognition.

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Section: Tear Diagnosismentioning

confidence: 99%

“…This methodology involves generating two histograms (horizontal and vertical histograms) of the knee images from the original MR scan. After smoothing and normalizing these histograms, their maximum and minimum values are used to approximately locate meniscal regions [14].…”

Section: Tear Diagnosismentioning

confidence: 99%

A Computer-Aided Type-II Fuzzy Image Processing for Diagnosis of Meniscus Tear

et al. 2016

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“…Kose et al 17 utilizan métodos de histograma, segmentación estadística y una plantilla triangular que aplican a la imagen para la detección de los desgarros del menisco en imágenes RM.…”

Section: Introductionunclassified

Segmentación de los huesos en imágenes TC empleando la umbralización global y adaptativa

et al. 2014

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Recibido el 2 de agosto de 2013; aceptado el 7 de marzo de 2014 Disponible en Internet el 28 de octubre de 2014 PALABRAS CLAVE Umbralización global; Umbralización adaptativa; Procesamiento de imágenes médicas; Reconstrucción tridimensional de huesos; Imágenes TC; Modelación geométrica; DescomposiciónResumen El tejido óseo es el elemento principal del esqueleto. Sirve de soporte a las partes blandas, protege órganos vitales y constituye un sistema de palancas que amplifica las fuerzas generadas durante la contracción muscular.La descripción del comportamiento mecánico del tejido óseo, mediante el empleo de modelos discretos, pasa por diferentes etapas de análisis, las cuales van desde el procesamiento digital de la imagen hasta la especificación de las propiedades físicas del tejido al modelo discreto, siendo la descomposición de estos modelos en sus partes constitutivas un elemento clave.En la presente investigación se discute un método para la descripción geométrica de los huesos a partir de una secuencia de imágenes de tomografía computarizada y basado en la combinación de la umbralización global y adaptativa.Los resultados obtenidos demostraron que este método constituye una propuesta efectiva al problema del volumen parcial y la separación de los huesos en las articulaciones. Segmentation of bone in CT images using global adaptive thresholdingAbstract Bone is the main element of the skeleton. It support soft tissues, protects vital organs and constitutes a lever system that amplifies forces generated during muscular contraction.A description is presented of the mechanical behavior of hard tissues by means of discrete models going through various stages of analysis, which range from digital image processing until the specification of physical properties of tissue to the discrete model. The decomposition of these models into their constituent parts being a key element.In this paper, we discuss a method for the geometric description of bones from a sequence of computed tomography images, combining global and adaptive thresholding to determine the geometric domain of bones in each slice. * Autor para correspondencia. Correo electrónico: dolgis@infomed.sld.cu (D.R. Ortega).

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“…As a matter of fact, using modern image processing techniques in ophthalmology gained significant interest especially in the last 15 years. The developments include automated diagnosing and monitoring systems for conditions such as degenerations, DR, ARMD etc, and detection of retinal landmarks such as optic disc, vascular network, macula and such (Köse et al, 2009), . The automated tools in ophthalmology have significant contributions in that they offer a great potential to be used in operations on large data set, which requires a substantial trained human effort when they are manually processed.…”

Section: Introductionmentioning

confidence: 99%

A Simple Analytic Approach for Tracking Retinal Vessels and Measuring Their Diameters

Yavuz

İkibaş

Köse

2010

Proceedings of the First International Conference on Bioinformatics

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Retinal image processing provides tools for automatic diagnosis and monitoring of retinal diseases such as diabetic retinopathy (DR), age related macular degeneration (ARMD), glucoma and such. The properties of vessel structures on the other hand are widely utilized in locating morphologic structures such as optic disc and macula and in automatic diagnosis of the retinal diseases. Due to the importance of retinal vessels, we propose a simple approach for vessel tracking and measuring vessel diameter in retinal fundus images. Images having manually segmented retinal vasculatures are obtained from STARE database and used in this study. Our method first finds the midlines of the vessel network on the segmented images by employing Zhang-Suen thinning algorithm and then tracks the vessel branches through those midlines. Lastly, the diameters of the vessel segments in different parts of the vasculature are calculated along with the tracking operation. The performed test results show that the proposed automatic method is quite successfully tracks the vessel network and measure the diameter.

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An automatic diagnosis method for the knee meniscus tears in MR images

Cited by 37 publications

References 24 publications

A Computer-Aided Type-II Fuzzy Image Processing for Diagnosis of Meniscus Tear

A Computer-Aided Type-II Fuzzy Image Processing for Diagnosis of Meniscus Tear

Segmentación de los huesos en imágenes TC empleando la umbralización global y adaptativa

A Simple Analytic Approach for Tracking Retinal Vessels and Measuring Their Diameters

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