Medical Image Segmentation by MarkerControlled Watershed and Mathematical Morphology

Allaoui, Ahmad El

doi:10.5121/ijma.2012.4301

Cited by 20 publications

(14 citation statements)

References 8 publications

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“…This algorithm considers a grey level image as a topographic relief. If we combine the grey level of each point at an altitude then it is possible to define the watershed transform as the ridge forming the boundary between two watersheds [28]. In this way, watershed of the said relief can be computed.…”

Section: Marker-controlled Watershed Segmentationmentioning

confidence: 99%

“…(2) Watershed by markers [30] In hierarchical approach, a tree of regions is generated from the result of the watershed [28]. First, the regions and watershed are merged.…”

Section: Marker-controlled Watershed Segmentationmentioning

confidence: 99%

“…The over segmentation problem means creations of too many regions. This over segmentation problem can be solved with the following two methods [28]:…”

Section: Marker-controlled Watershed Segmentationmentioning

confidence: 99%

See 2 more Smart Citations

A New Approach towards Clustering based Color Image Segmentation

Bora¹,

Gupta²

2014

IJCA

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ABSTRACT:Color image segmentation is currently a very emerging topic for researchers in Image processing. Clustering is a frequently chosen methodology for this image segmentation task. But for a better segmentation, there arises the need of an optimal technique. In this paper, we propose an integrated approach for color image segmentation which is a new of its kind. Here, we integrate the famous k-means algorithm with watershed algorithm. But, here we chose 'cosine' distance measure for k-means algorithm to optimize the segmented result of the later one. Also, as color space has a leading impact on color image segmentation task, so, we chose HSV color space for our proposed approach. Since usually the noise arises during the segmentation process, so here the final segmented image is filtered by median filter to make the output image clearer and noise free. The result of the proposed approach is found to be quite satisfactory.

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Section: Marker-controlled Watershed Segmentationmentioning

confidence: 99%

“…(2) Watershed by markers [30] In hierarchical approach, a tree of regions is generated from the result of the watershed [28]. First, the regions and watershed are merged.…”

Section: Marker-controlled Watershed Segmentationmentioning

confidence: 99%

See 1 more Smart Citation

A New Approach towards Clustering based Color Image Segmentation

Bora¹,

Gupta²

2014

IJCA

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“…Choose the shape and size of the structuring element is very important: it affects the outcome; it may take the form of a disk of radius R, rectangular or other shape. There are many tools morphological image segmentation is corrosion, expansion, reconstruction (Aliiaout and Nasri, 2012). …”

Section: Mathematical Morphologymentioning

confidence: 99%

Automatic Segmentation of Skin Lesions Using Histogram Thresholding

Abbadi¹

2014

Journal of Computer Science

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Automatic lesion segmentation is an important part of computer-based image analysis of pigmented skin lesions. Currently, there is a great interest in the development of Computer-Aided Diagnosis (CAD) systems for dermoscopic images. The segmentation step is one of the most important ones, since its accuracy determines the eventual success or failure of a CAD system. This study introduced new method of dermoscopic images segmentation. The preprocess was the filtering operation to dermoscopy image to remove most of difficulties facing the efficient segmentations, like a variety of lesion shapes, sizes, color, changes due to different skin types and textures and presence of hairs. Segmentation based mainly on histogram thresholding. The enhancements of image achieved by using mathematical morphology in order to obtain better segmentation with smooth border and without any noise in the lesion region. The proposed method evaluated by using Hammoude Distance (HM) and the True Detection Rate (TDR). Also the proposed method is compared with other skin lesions segmentation methods such as Otsu, adaptive thresholding and fuzzy Cmeans. The accuracy of proposed method was 96.32%, which is highly promised result and dependable.

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“…The watershed transform implement these ideas to gray-scale image processing [18] in a way that can solve a lot of image segmentation problems. Segmentation by watershed method is fast, reliable and extensively employed in image processing and analysis [19]. Watershed is included in adaptive shape segmentation and it is a powerful technique where it combines the frontier approach and the region approach, watershed makes a fast detection of both edges and regions.…”

Section: Introductionmentioning

confidence: 99%

Segmentation of Complementary DNA Microarray Images using Marker-Controlled Watershed Technique

SaadEl-Gawady¹,

Eltoukhy²,

El-Tawel³

et al. 2015

IJCA

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DNA microarray image processing becomes a viable branch of bioinformatics, its importance stems from the fact that it allows viewing and measuring tens of thousands of genes concurrently. Many techniques were introduced to develop and improve the mission of processing DNA microarray images. The aim of this study is to make a segmentation of the cDNA microarray images. The Marker Controlled Watershed technique is used to segment the DNA microarray spots. The proposed method starts with preprocessing step; i.e. denoising and histogram equalization. Then, the spots are segmented from its background. The used images in this paper were obtained from Stanford Microarray Database (SMD). The obtained results of the developed method are compared to the results of K-means clustering method and fuzzy c-means clustering method. We can conclude that the Marker Controlled Watershed technique is efficient for segmenting the cDNA microarray images.

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Medical Image Segmentation by MarkerControlled Watershed and Mathematical Morphology

Abstract: ABSTRACT

Cited by 20 publications

References 8 publications

A New Approach towards Clustering based Color Image Segmentation

A New Approach towards Clustering based Color Image Segmentation

Automatic Segmentation of Skin Lesions Using Histogram Thresholding

Segmentation of Complementary DNA Microarray Images using Marker-Controlled Watershed Technique

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