Graph partitioning active contours (GPAC) for image segmentation

Sumengen, Baris; Manjunath, B.S.

doi:10.1109/tpami.2006.76

Cited by 74 publications

(69 citation statements)

References 25 publications

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“…Perubahan tersebut dikontrol dengan meminimumkan suatu fungsi objektif yang menggabungkan informasi lokal pada citra sebagai energi internal dan a priori pengetahuan tentang lokasi, ukuran dan bentuk umum dari struktur sebagai energi eksternal. Fungsi objektif pada prinsipnya adalah mencari suatu parameter optimal yang membuat kesetimbangan antara energi internal dan energi eksternal [4,[7][8][9]. Dalam aplikasi model active contour untuk segmentasi citra medik, masalah yang dihadapi adalah bagaimana energi internal dan energi eksternal dapat terdefinisi dengan baik dengan beban komputasi yang relatif ringan.…”

Section: Pendahuluan Teknologi Medical Imaging Seperti Computerized Tunclassified

“…, ( 1 (9) Luaran nilai dari pusat piksel pada window adalah merupakan nilai rata-rata pada daerah yang memiliki variansi terkecil. Dengan kata lain, ∇f(x,y) hitung setelah f(x,y) difilter dengan filter Kuwahara.…”

Section: Metode Penelitianunclassified

“…Untuk mengatasi kelemahan definisi penggunaan ∇f(x,y) sebagai gaya eksternal, suatu metode yang memperbesar jangkauan dari medan vektor relatif terhadap batas tepian dari objek yang dikenal dengan metode gradient vector flow (GVF) [4,9,12].…”

Section: Metode Penelitianunclassified

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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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Section: Pendahuluan Teknologi Medical Imaging Seperti Computerized Tunclassified

Section: Metode Penelitianunclassified

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

Suprijanto

Muchtadi

Setiawan

2010

MST

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“…Given the NP-completeness of this problem, spectral graph theory is applied to achieve an approximate solution. In [26], Sumengen and Manjunath present a graph-partitioning active contour approach to iteratively search for a locally optimal boundary that minimizes the grouping cost used in normalized cut. In [27], Chan and Vese define a grouping cost that requires the resulting boundary to be both smooth and enclose a region with homogeneous intensity.…”

Section: Related Workmentioning

confidence: 99%

Edge Grouping Combining Boundary and Region Information

Stahl

Wang

2007

IEEE Trans. on Image Process.

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Abstract-This paper introduces a new edge-grouping method to detect perceptually salient structures in noisy images. Specifically, we define a new grouping cost function in a ratio form, where the numerator measures the boundary proximity of the resulting structure and the denominator measures the area of the resulting structure. This area term introduces a preference towards detecting larger-size structures and, therefore, makes the resulting edge grouping more robust to image noise. To find the optimal edge grouping with the minimum grouping cost, we develop a special graph model with two different kinds of edges and then reduce the grouping problem to finding a special kind of cycle in this graph with a minimum cost in ratio form. This optimal cycle-finding problem can be solved in polynomial time by a previously developed graph algorithm. We implement this edge-grouping method, test it on both synthetic data and real images, and compare its performance against several available edge-grouping and edge-linking methods. Furthermore, we discuss several extensions of the proposed method, including the incorporation of the well-known grouping cues of continuity and intensity homogeneity, introducing a factor to balance the contributions from the boundary and region information, and the prevention of detecting self-intersecting boundaries.

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“…Soille and Vogt (2009) presents a method for segmenting binary patterns into seven mutually exclusive categories; core, islet, loop, bridge, perforation, edge and branch.They achieved by applying a series of morphological transformations such as erosions, geodesic dilations, reconstruction by dilation, anchored skeletonisation. Sumengen and Manjunath (2006) introduces new type of variational segmentation cost functions and associated active contour methods that are based on pair wise similarities or dissimilarities of the pixels. Also extens a new curve evolution framework, the graph partitioning active contours.…”

Section: Introductionmentioning

confidence: 99%

An Alternative Graph Cut Algorithm for Morphological Edge Detection

Radhakrishnan¹

2012

American Journal of Applied Sciences

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Problem statement: Image analysis has some basic importance of edge detection. Characterize the object boundaries are helpful for object segmentation, registration and identification in a scene. There are many methods for edge detection, but it can be identified by two major methods, search-based and zero-crossing based. Approach: Proposed method tries to follow searching the edge points by applying graph cuts in the place of morphological approach. The graph cut edge detection algorithm is very effective to detect edges with minimum searches. Results: The new approach produces connectives edge boundary with recursive approach in short duration comparatively with morphological approach. It is obtained by identifying the edge points with cut measure within its boundary. Conclusion: The graph cut algorithm detect boundary for any shapes with normal structure, this identification of edges in a complex background is always required a lot of user inputs. In each level only the local neighboring boundaries to be separated with labels. It is optimized approach for unknown region boundaries in complex basins.

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Graph partitioning active contours (GPAC) for image segmentation

Cited by 74 publications

References 25 publications

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

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

Edge Grouping Combining Boundary and Region Information

An Alternative Graph Cut Algorithm for Morphological Edge Detection

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