Knee Articular Cartilage Segmentation with Priors Based On Gaussian Kernel Level Set Algorithm

Segmentation of regions of interest is a well-known problem in image segmentation. This paper presents a region-based image segmentation technique using active contours with signed pressure force (SPF) function. The proposed algorithm contemporaneously traces high intensity or dense regions in an image by evolving the contour inwards. In medical image modalities these high intensity or dense regions refer to tumor, masses, or dense tissues. The proposed method partitions an image into an arbitrary number of subregions and tracks down salient regions step by step. It is implemented by enforcing a new region-based SPF function in a traditional edge-based level set model. It partitions an image into subregions and then discards outer subregion and partitions inner region into two more subregions; this continues iteratively until a stopping condition is fulfilled. A Gaussian kernel is used to regularize the level set function, which not only regularizes it but also removes the need of computationally expensive reinitialization. The proposed segmentation algorithm has been applied to different images in order to demonstrate the accuracy, effectiveness, and robustness of the algorithm.

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Active Contours Using Harmonic Global Division Function

Engineering, Technology & Applied Science Research ◽

10.48084/etasr.2866 ◽

2019 ◽

Vol 9 (4) ◽

pp. 4457-4462

Author(s):

M. T. Bhatti ◽

S. Soomro ◽

A. M. Bughio ◽

T. A. Soomro ◽

A. Anwar ◽

...

Keyword(s):

Level Set ◽

Active Contours ◽

State Of The Art ◽

Gaussian Kernel ◽

Pressure Force ◽

Harmonic Intensity ◽

Low Contrast ◽

Intensity Information ◽

New Energy ◽

Previous State

This paper presents the region-based active contours method based on the harmonic global signed pressure force (HGSPF) function. The proposed formulation improves the performance of the level set method by utilizing intensity information based on the global division function, which has the ability to segment out regions with higher intensity differences. The new energy utilizes harmonic intensity, which can better preserve the low contrast details and can segment complicated areas easily. A Gaussian kernel is adjusted to regularize level set and to escape an expensive reinitialization. Finally, a set of real and synthetic images are used for validation of the proposed method. Results demonstrate the performance of the proposed method, the accuracy values are compared to previous state-of-the-art methods.

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Segmentation of spinal cord from computed tomography images based on level set method with Gaussian kernel

Soft Computing ◽

10.1007/s00500-020-05113-1 ◽

2020 ◽

Vol 24 (24) ◽

pp. 18811-18820

Author(s):

V. Malathy ◽

M. Anand ◽

N. Dayanand Lal ◽

Zameer Ahmed Adhoni

Keyword(s):

Computed Tomography ◽

Spinal Cord ◽

Level Set Method ◽

Level Set ◽

Gaussian Kernel ◽

Computed Tomography Images

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A LEVEL-SET METHOD BASED ON GLOBAL AND LOCAL REGIONS FOR IMAGE SEGMENTATION

International Journal of Pattern Recognition and Artificial Intelligence ◽

10.1142/s021800141255004x ◽

2012 ◽

Vol 26 (01) ◽

pp. 1255004 ◽

Cited By ~ 5

Author(s):

YU QIAN ZHAO ◽

XIAO FANG WANG ◽

FRANK Y. SHIH ◽

GANG YU

Keyword(s):

Image Segmentation ◽

Level Set Method ◽

Level Set ◽

Outer Region ◽

Descent Method ◽

Gaussian Kernel ◽

Gradient Descent Method ◽

Low Contrast ◽

Global And Local ◽

Cv Model

This paper presents a new level-set method based on global and local regions for image segmentation. First, the image fitting term of Chan and Vese (CV) model is adapted to detect the image's local information by convolving a Gaussian kernel function. Then, a global term is proposed to detect large gradient amplitude at the outer region. The new energy function consists of both local and global terms, and is minimized by the gradient descent method. Experimental results on both synthetic and real images show that the proposed method can detect objects in inhomogeneous, low-contrast, and noisy images more accurately than the CV model, the local binary fitting model, and the Lankton and Tannenbaum model.

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A New Kernel-Based Fuzzy Level Set Method for Automated Segmentation of Medical Images in the Presence of Intensity Inhomogeneity

Computational and Mathematical Methods in Medicine ◽

10.1155/2014/978373 ◽

2014 ◽

Vol 2014 ◽

pp. 1-14 ◽

Cited By ~ 5

Author(s):

Maryam Rastgarpour ◽

Jamshid Shanbehzadeh

Keyword(s):

Image Segmentation ◽

Level Set ◽

Medical Image ◽

Medical Images ◽

Medical Image Segmentation ◽

Gaussian Kernel ◽

Intensity Inhomogeneity ◽

Integrative Approach ◽

Robust Segmentation ◽

Level Set Evolution

Researchers recently apply an integrative approach to automate medical image segmentation for benefiting available methods and eliminating their disadvantages. Intensity inhomogeneity is a challenging and open problem in this area, which has received less attention by this approach. It has considerable effects on segmentation accuracy. This paper proposes a new kernel-based fuzzy level set algorithm by an integrative approach to deal with this problem. It can directly evolve from the initial level set obtained by Gaussian Kernel-Based FuzzyC-Means (GKFCM). The controlling parameters of level set evolution are also estimated from the results of GKFCM. Moreover the proposed algorithm is enhanced with locally regularized evolution based on an image model that describes the composition of real-world images, in which intensity inhomogeneity is assumed as a component of an image. Such improvements make level set manipulation easier and lead to more robust segmentation in intensity inhomogeneity. The proposed algorithm has valuable benefits including automation, invariant of intensity inhomogeneity, and high accuracy. Performance evaluation of the proposed algorithm was carried on medical images from different modalities. The results confirm its effectiveness for medical image segmentation.

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The Effect of Total Meniscectomy versus Caudal Pole Hemimeniscectomy on the Stifle Joint of the Sheep

Veterinary and Comparative Orthopaedics and Traumatology ◽

10.1055/s-0038-1632463 ◽

1999 ◽

Vol 12 (02) ◽

pp. 56-63 ◽

Cited By ~ 5

Author(s):

C. R. Bellenger ◽

P. Ghosh ◽

Y. Numata ◽

C. Little ◽

D. S. Simpson

Keyword(s):

Tissue Culture ◽

Articular Cartilage ◽

Fibrous Tissue ◽

Cartilage Degeneration ◽

Medial Compartment ◽

Proteoglycan Synthesis ◽

Stifle Joint ◽

Medial Meniscectomy ◽

Tibial Condyle ◽

Articular Cartilage Degeneration

SummaryTotal medial meniscectomy and caudal pole hemimeniscectomy were performed on the stifle joints of twelve sheep. The two forms of meniscectomy produced a comparable degree of postoperative lameness that resolved within two weeks of the operations. After six months the sheep were euthanatised and the stifle joints examined. Fibrous tissue that replaced the excised meniscus in the total meniscectomy group did not cover as much of the medial tibial condyle as the residual cranial pole and caudal fibrous tissue observed following hemimeniscectomy. The articular cartilage from different regions within the joints was examined for gross and histological evidence of degeneration. Analyses of the articular cartilage for water content, glycosaminoglycan composition and DNA content were performed. The proteoglycan synthesis and release from explanted articular cartilage samples in tissue culture were also measured. There were significant pathological changes in the medial compartment of all meniscectomised joints. The degree of articular cartilage degeneration that was observed following total meniscectomy and caudal pole meniscectomy was similar. Caudal pole hemimeniscectomy, involving transection of the meniscus, causes the same degree of degeneration of the stifle joint that occurs following total meniscectomy.The effect of total medial meniscectomy versus caudal pole hemimeniscectomy on the stifle joint of sheep was studied experimentally. Six months after the operations gross pathology, histopathology, cartilage biochemical analysis and the rate of proteoglycan synthesis in tissue culture were used to compare the articular cartilage harvested from the meniscectomised joints. Degeneration of the articular cartilage from the medial compartment of the joints was present in both of the groups. Caudal pole hemimeniscectomy induces a comparable degree of articular cartilage degeneration to total medial meniscectomy in the sheep stifle joint.

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