Application of active contour models in medical image segmentation

Derraz, Foued; Beladgham, Mohammed; Khelif, M

doi:10.1109/itcc.2004.1286732

Cited by 50 publications

(19 citation statements)

References 19 publications

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“…The average distance error (e) [31] is used to validate the accuracy of the test results. It is given by: (12) where v(s) represents the original contour position of the deformed snake, v (s) represents the extracted boundary of the target object and L is the length of the snake. The average distance error provides a measure of the accuracy of the final position of the snake (or in other words, the final boundary extraction results) with respect to the true boundary of the object.…”

Section: Resultsmentioning

confidence: 99%

“…These new techniques are able to improve the efficiency and accuracy of the computer aided medical diagnosis process. Conventionally, most of these improvements were introduced to segment different regions of the brain, heart, womb, locating tumor regions, etc [8][9][10][11][12][13]. However, very few attempts were given to segment discontinuous boundaries of bone fractures in the past literature.…”

Section: Introductionmentioning

confidence: 99%

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A deformable model to segment discontinuous boundaries of bone fractures in X-ray images

Niroshika

Meegama

2013

2013 IEEE 8th International Conference on Industrial and Information Systems

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Very often, a bone fracture results in discontinuous boundaries between two or more bone structures. It is required to capture such fractures using an imaging technology and subsequently model the fractured locations to detect the accurate physical shape of the fractured region. In this paper, an algorithm is proposed based on active contours, often referred to as snakes, to detect discontinuous boundaries of bone fractures from X-ray images. The proposed technique has the ability to incorporate a prior knowledge of significant corners of the fracture into the deforming contour in order to deform it towards boundaries of the object without surpassing points at discontinuous regions. Experimental results with several X-ray images show the ability of the new technique to extract features of interest with increased accuracy.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A deformable model to segment discontinuous boundaries of bone fractures in X-ray images

Niroshika

Meegama

2013

2013 IEEE 8th International Conference on Industrial and Information Systems

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“…Lebih dari itu, pada citra medik untuk satu irisan tertentu, semua jaringan atau organ yang terdapat pada irisan tersebut akan direkonstruksi pada citra, sehingga menyebabkan analisis secara visual untuk objek jaringan/organ yang menjadi perhatian relatif sulit dilakukan. Kondisi ini boleh jadi menyebabkan masalah konsistensi dan keakuratan dalam melakukan analisis citra untuk kasus yang sama pada pasien berbeda [2][3][4][5].…”

Section: Pendahuluan Teknologi Medical Imaging Seperti Computerized Tunclassified

Segmentasi Citra Secara Semi-Otomatis Untuk Visualisasi Volumetrik Citra Ct-Scan Pelvis

Suprijanto

Muchtadi

Setiawan

2010

MST

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AbstrakPerkembangan terkini dari perangkat pencitraan medik computerized tomography (CT) scan telah memungkinkan dihasilkannya citra dari penampang melintang secara multi irisan dalam orde beberapa detik. Citra medik digital yang dihasilkan merepresentasikan penampang melintang dari berbagai struktur jaringan dari irisan yang dicitrakan. Salah satu tantangan yang dapat membantu dalam proses diagnosis berbasis citra adalah ekstraksi informasi dari struktur anatomi tertentu dengan suatu metode segmentasi citra serta visualisasi volumetrik dengan bantuan komputer. Untuk kasus visualisasi volumetrik tulang pelvis pada citra CT-scan multi irisan, seluruh citra yang mengandung bagian struktur tulang pelvis harus disegmentasi. Pada penelitian ini, satu teknik segmentasi citra berbasis active contour akan diimplementasikan untuk melakukan segmentasi citra multi irisan secara semi otomatis. Proses segmentasi citra diawali dengan menentukan model kurva 2D yang dilakukan secara manual pada citra irisan pertama. Kemudian model kurva tersebut secara iterasi akan berdeformasi sampai dengan bentuk kurva yang berhimpit pada batas tepian citra tulang pelvis. Hari akhir kurva 2D pada irisan pertama akan digunakan sebagai inisialisasi model kurva 2D pada proses segmentasi citra irisan berikutnya. Proses tersebut akan berlanjut sampai dengan citra irisan terakhir. Metode segmentasi citra berbasis active contour akan dibandingkan dengan metode segmentasi secara nilai ambang dari homogenitas distribusi intensitas dan metode segmentasi secara manual. Analisis secara kualitatif terhadap hasil segmentasi tiap irisan dan analisis kualitatif pada representasi visualisasi volumetrik digunakan pada penelitian ini. AbstractSemi-Automatic Image Segmentation for Volumetric Visualization of Pelvis CT Scan-Images. The current development of computerized tomography (CT) has enable us to obtain cross sectional image using multi slicing techniques in an order of few seconds. The obtained images represent several tissue structures on cross section slice being imaged. One challenge to help diagnosis using CT images is extracting an anatomic structure of interest using a method of image segmentation and volumetric visualization with the assistance of computers. In case of volumetric visualization of pelvis bones extracted from multi-slice CT images, whole images which are containing part of pelvis bone structures must be segmented. In this research, an image segmentation technique based on active contour is implemented for semi-automatic multi slice image segmentation. Image segmentation steps are initialized with a define model of 2D curve on the first slice image manually. Next, its model curve is deformed to reach the final result of 2D curve that fits to boundary edges of pelvis bone image. The final result of 2D curve on previous slice image was used as an initialization model of 2D curve on the next slice images. This process will continue until the final slice image. This segmentation method was compared with the segmentation method based on th...

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“…Image processing in medical science is significantly applied to magnetic resonance (MR) as images of the brain. Image segmentation is given much consideration and care during the last twenty years [23][24][25][26]. Biomedical images are formed by objects of varying shapes, sizes and intensities [1]- [4].…”

Section: Introductionmentioning

confidence: 99%

Extraction of Brain Tumour in MRI Images using Marker Controlled Watershed Transform Technique in MATLAB

Maheshwari

Shah

Shaikh

et al. 2015

JBEMi

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In recent years, substantial research has been carried out in the field of image processing to evaluate different structures and information from images. Image processing techniques have played a pivotal role in a wide range of medical image applications. They have been widely used to design different computational algorithms for extracting clinical information from medical images in different modalities including MRI, CT and Ultrasound. This paper aims to propose the use of image processing techniques in the medical field. The objective of this paper is to develop a MATLAB based algorithm that can be used to extract a brain tumor from a MRI Image. In this research, we have performed some noise removal functions, segmentation techniques and morphological operations for detection and extraction which are the basic concepts of image processing. We have developed a Watershed Transform technique based on internal and external markers. The detection and extraction of tumor from MRI image of the brain is done by using MATLAB software.

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Application of active contour models in medical image segmentation

Cited by 50 publications

References 19 publications

A deformable model to segment discontinuous boundaries of bone fractures in X-ray images

A deformable model to segment discontinuous boundaries of bone fractures in X-ray images

Segmentasi Citra Secara Semi-Otomatis Untuk Visualisasi Volumetrik Citra Ct-Scan Pelvis

Extraction of Brain Tumour in MRI Images using Marker Controlled Watershed Transform Technique in MATLAB

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