Split-and-Merge Segmentation of Magnetic Resonance Medical Images: Performance Evaluation and Extension to Three Dimensions

Manousakas, Ioannis; Undrill, P.E.; Cameron, G. Gordon; Redpath, Thomas William

doi:10.1006/cbmr.1998.1489

Cited by 72 publications

(25 citation statements)

References 41 publications

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“…The region growing algorithm has been proven to be an effective approach for image segmentation [7] . Region growing is a technique for extracting a connected region in the image that is connected based on some predefined criteria regarding intensity.…”

Section: Region Growingmentioning

confidence: 99%

Medical Image Processing and Analysis

Tian

Wang

Dai

et al. 2013

Advanced Topics in Science and Technology in China

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Generally, medical imaging refers to the specialized techniques and instrumentation used to create images or information of the human body for clinical purposes or medical science (including the study of normal anatomy and function) [1] . For clinical purposes, medical images of specific tissues or organs are obtained to assist in diagnosing a disease or specific pathology.There are many medical imaging modalities and techniques which have been developed in the past few years. The discovery of X-rays by the German physicist Wilhelm Konrad Rontgen and their application to medical examination, new medical imaging methods and devices such as CT, PET, MRI and others promoted the rapid development of Medical Imaging Science and evoked revolutionary changes in traditional medical diagnosis and treatment. With the help of these methods and devices, the images of organs and tissues in the human body can be acquired non-invasively, so as to get more objective information of the body's physiological status and pathological changes, and finally to assist in diagnosis and treatment.The selection of an appropriate medical imaging modality is important for obtaining the target information for a successful pathological investigation. Anatomical structures can be effectively imaged with CT, MRI, ultrasound and optical imaging methods. Furthermore, information about physiological structures with respect to metabolic functions can be obtained through nuclear medicine SPECT and PET, ultrasound, optical fluorescence and several derivative protocols J. Tian, Molecular Imaging

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Section: Region Growingmentioning

confidence: 99%

Medical Image Processing and Analysis

Tian

Wang

Dai

et al. 2013

Advanced Topics in Science and Technology in China

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“…In contour-based segmentation the active contour algorithm is a typical and widely used in literature [6] [7]. Secondly in region-based segmentation many techniques have been developed like region growing technique [8], split-and-merge based technique [9], and watershed based technique [10]. And Finally Classification-based segmentation includes statistical based classification such as in [11] Markov random field is used, in [12] Hidden Markov models based technique is proposed, and in [13] Gaussian mixture model segmentation is proposed.…”

Section: Related Workmentioning

confidence: 99%

Anisotropic Diffusion based Brain MRI Segmentation and 3D Reconstruction

Jaffar¹,

Zia²,

Latif³

et al. 2012

IJCIS

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In medical field visualization of the organs is very imperative for accurate diagnosis and treatment of any disease. Brain tumor diagnosis and surgery also required impressive 3D visualization of the brain to the radiologist. Detection and 3D reconstruction of brain tumors from MRI is a computationally time consuming and error-prone task. Proposed system detects and presents a 3D visualization model of the brain and tumor inside which greatly helps the radiologist to effectively diagnose and analyze the brain tumor. We proposed a multi-phase segmentation and visualization technique which overcomes the many problems of 3D volume segmentation methods like lake of fine details. In this system segmentation is done in three different phases which reduces the error chances. The system finds contours for skull, brain and tumor. These contours are stacked over and two novel methods are used to find the 3D visualization models. The results of these techniques, particularly of interpolation based, are impressive. Proposed system is tested against publically available data set [41] and MRI datasets available from MRI & CT center Rawalpindi, Pakistan [42].

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“…Many image processing techniques have been proposed for MR image segmentation, most notably thresholding [3,4], region-growing [5], edge detection [6], pixel classification [7,8] and clustering [9,10,11]. Some algorithms using the neural network approach have also been investigated in the MR image segmentation problems [12,13].…”

Section: Introductionmentioning

confidence: 99%

Maximum Class Separability for Rough-Fuzzy C-Means Based Brain MR Image Segmentation

Maji

Pal

2008

Transactions on Rough Sets IX

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Abstract. Image segmentation is an indispensable process in the visualization of human tissues, particularly during clinical analysis of magnetic resonance (MR) images. In this paper, the rough-fuzzy c-means (RFCM) algorithm is presented for segmentation of brain MR images. The RFCM algorithm comprises a judicious integration of the rough sets, fuzzy sets, and c-means algorithm. While the concept of lower and upper approximations of rough sets deals with vagueness and incompleteness in class definition of brain MR images, the membership function of fuzzy sets enables efficient handling of overlapping classes. The crisp lower bound and fuzzy boundary of a class, introduced in the RFCM algorithm, enable efficient segmentation of brain MR images. One of the major issues of the RFCM based brain MR image segmentation is how to select initial prototypes of different classes or categories. The concept of discriminant analysis, based on the maximization of class separability, is used to circumvent the initialization and local minima problems of the RFCM. Some quantitative indices are introduced to extract local features of brain MR images for accurate segmentation. The effectiveness of the RFCM algorithm, along with a comparison with other related algorithms, is demonstrated on a set of brain MR images.

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Split-and-Merge Segmentation of Magnetic Resonance Medical Images: Performance Evaluation and Extension to Three Dimensions

Cited by 72 publications

References 41 publications

Medical Image Processing and Analysis

Medical Image Processing and Analysis

Anisotropic Diffusion based Brain MRI Segmentation and 3D Reconstruction

Maximum Class Separability for Rough-Fuzzy C-Means Based Brain MR Image Segmentation

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