An automated apparatus for cancer prescreening: CYBEST

Watanabe, Sadakazu

doi:10.1016/0146-664x(74)90029-x

Cited by 71 publications

(23 citation statements)

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“…This has also been studied by Weska and Rosenfeld [45]. Several variations of the above method have been proposed [46,47,48] Weszka and Rosenfeld [49] unified them.…”

Section: Thresholding Techniquementioning

confidence: 97%

A Methodological Survey and Proposed Algorithm on Image Segmentation using Genetic Algorithm

Agarwal¹,

Singh²

2013

IJCA

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This literature review attempts to provide a brief overview of some of the most common image segmentation techniques. It discusses Edge detection technique, Thresholding technique, Region growing based technique, Watershed technique, Compression based method, Histogram based segmentation and Graph partitioning method. With the growing research on image segmentation, it has become important to categorise the research outcomes and provide readers with an overview of the existing segmentation techniques in each category. In this paper different method of implementing genetic algorithm has been reviewed. Finally, summaries and review of research work on wrapper approach for image segmentation techniques has been represented.

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“…This has also been studied by Weska and Rosenfeld [45]. Several variations of the above method have been proposed [46,47,48] Weszka and Rosenfeld [49] unified them.…”

Section: Thresholding Techniquementioning

confidence: 97%

A Methodological Survey and Proposed Algorithm on Image Segmentation using Genetic Algorithm

Agarwal¹,

Singh²

2013

IJCA

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“…al. [2], [7] proposed the difference histogram method, which selects threshold at the gray level with the maximal amount of difference. These techniques utilize the information concerning neighboring pixels or edges in the original picture to modify the histogram so as to make it useful for thresholding.…”

Section: Related Workmentioning

confidence: 99%

A Novel GA Based OCR Enhancement and Segmentation Methodology for Marathi Language in Bimodal Framework

Singh

Bacchuwar

Choubey

2011

Information Systems for Indian Languages

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Abstract. Automated learning systems used to extract information from images play a major role in document analysis. Optical character recognition or OCR has been widely used to automatically segment and index the documents from a wide space. Most of the methods used for OCR recognition and extraction like HMM's, Neural etc, mentioned in literature have errors which require human operators to be rectified and fail to extract images with blur as well as illumination variance. This paper explains proposes an enhancement supported threshold based pre-processing methodology for word spotting in Marathi printed bimodal images using image segmentation. The methodology makes use of an enhanced image obtained by histogram equalization followed by followed by age segmentation using a specific threshold. The threshold can be obtained using genetic algorithms. GA based segmentation technique is codified as an optimization problem used efficiently to search maxima and minima from the histogram of the image to obtain the threshold for segmentation. The system described is capable of extracting normal as well as blurred images and images for different lighting conditions. The same inputs are tested for a standard GA based methodology and the results are compared with the proposed method. The paper further elaborates the limitations of the method.

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“…In multilevel thresholding, one can select more than one threshold and use them to divide the whole range of gray values into several sub ranges. Most of the thresholding techniques [5] - [8] utilize shape information of the histogram of the given image to determine appropriate threshold.…”

Section: Introductionmentioning

confidence: 99%

“…S. Watanable et al [5] proposed the difference histogram method which selects threshold at the gray level with the maximal amount of difference. These methods utilize information concerning neighbouring pixels or edges of the original picture to modify the histogram so as to make it suitable for determination of optimal threshold.…”

Section: Introductionmentioning

confidence: 99%

Classification of Objects and Background Using Parallel Genetic Algorithm Based Clustering

Kanungo¹,

Nanda²,

Ghosh³

2007

ELCVIA

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In this paper, two novel strategies have been proposed to segment the object and background in a given scene. The first one, known as Featureless (FL) approach, deals with the histogram of the original image where Parallel Genetic Algorithm (PGA) based clustering notion is used to determine the optimal threshold from the discrete nature of the histogram distribution. In this regard, we have proposed a new interconnection model for PGA. The second scheme, the Featured Based (FB) approach, is based on the proposed feature histogram distribution. A feature from the given image is extracted and the histogram corresponding to the derived feature is used to determine the optimal threshold of the original image. The proposed PGA based clustering is used to determine the optimal threshold. The performance of both the schemes is compared with that of Otsu's and Kwon's method and FB method is found to be the best among the three techniques.

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An automated apparatus for cancer prescreening: CYBEST

Cited by 71 publications

References 1 publication

A Methodological Survey and Proposed Algorithm on Image Segmentation using Genetic Algorithm

A Methodological Survey and Proposed Algorithm on Image Segmentation using Genetic Algorithm

A Novel GA Based OCR Enhancement and Segmentation Methodology for Marathi Language in Bimodal Framework

Classification of Objects and Background Using Parallel Genetic Algorithm Based Clustering

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