Image segmentation using fuzzy correlation

Pal, Sankar K.; Ghosh, Ashish

doi:10.1016/0020-0255(92)90017-3

Cited by 43 publications

(12 citation statements)

References 9 publications

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“…Poor image quality could be a function of several factors: variation in staining, nonuniform illumination of the sample, or nonlinear quantitation of the image capturing device. A number of techniques could be used to improve image quality and normalize image variations (8)(9)(10). With these techniques some inferior images could be improved to the point that they could be evaluated.…”

Section: Quality Of Image Evaluationmentioning

confidence: 99%

Semi‐automated imaging system to quantitate Her‐2/neu membrane receptor immunoreactivity in human breast cancer

Joshi¹,

Sharangpani²,

Porter

et al. 2007

Cytometry Pt A

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Background: Immunocytochemical methods for quantitating Her-2/neu immunoreactivity rest on subjective semi-quantitative interpretations with resulting interobserver, intraobserver, and fatigue variability. Methods: To standardize and quantitate measurements of Her-2/neu immunoreactivity, we created epithelial-recognition and specific membrane-recognition algorithms, which could image breast cancer cells against a background of stroma, compartmentalize the cancer cell into nucleus, cytoplasm and membrane, and quantitate the degree of Her-2/neu membrane immunoreactivity based on both gray scale intensity and RGB colorimetric determinations. Image acquisition utilized either scanner or microscope with attached camera with a resolution of 20 pixels/10 lm. Areas of 150 whole slides were screened and the regions of interest manually selected for image processing. Three hundred TMA cores were directly processed. Images were acquired by jpg conversion of svs virtual slides or direct jpg photomicrograph capture. Images were then assessed for quality and processed. Results: The digital algorithms successfully created a semi-automated imaging system whose algorithm-based ordinal values for Her-2/neu both strongly correlated with the subjective measurements (intraclass correlation: 0.84;

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Section: Quality Of Image Evaluationmentioning

confidence: 99%

Semi‐automated imaging system to quantitate Her‐2/neu membrane receptor immunoreactivity in human breast cancer

Joshi¹,

Sharangpani²,

Porter

et al. 2007

Cytometry Pt A

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“…However, in the real world problems, the separation of the clusters is usually fuzzy. Fuzzy clustering analysis has been extensively studied by many researchers (Bezdek & Pal, 1992;Huntsberger et Al., 1993;Moghaddam Zadeh & Bourbakis, 1997;Nguyen & Cohen, 1997;Pal & Ghosh, 1992). The most commonly used fuzzy clustering algorithm is fuzzy C-means (FCM), developed by Bezdek (1993).…”

Section: Initiator and Clustering Agentsmentioning

confidence: 99%

A Multi-Agent Expert System for Steel Grade Classification Using Adaptive Neuro-fuzzy Systems

Hossein¹,

Zarandi²,

Avazbeigi³

et al. 2010

Expert Systems

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“…Fuzzy set theory gives a mechanism to represent ambiguity within an image [11]. Each pixel of an image has a degree of belongingness (membership) to a region or a boundary.…”

Section: Fuzzy Based Techniquesmentioning

confidence: 99%

An Adaptive Color Image Segmentation

Deshmukh¹,

Shinde²

2006

ELCVIA

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A novel Adaptive Color Image Segmentation (ACIS) System for color image segmentation is presented. The proposed ACIS system uses a neural network with architecture similar to the multilayer perceptron (MLP) network. The main difference is that neurons here uses a multisigmoid activation function. The multisigmoid function is the key for segmentation. The number of steps i.e. thresholds in the multisigmoid function are dependant on the number of clusters in the image. The threshold values for detecting the clusters and their labels are found automatically from the first order derivative of histograms of saturation and intensity in the HSV color space. Here, the main use of neural network is to detect the number of objects automatically from an image. The advantage of this method is that no a priori knowledge is required to segment the color image. ACIS label the objects with their mean colors. The algorithm is found to be reliable and works satisfactorily on different kinds of color images. Experimental results show that the performance of ACIS is robust on noisy images also.

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Image segmentation using fuzzy correlation

Cited by 43 publications

References 9 publications

Semi‐automated imaging system to quantitate Her‐2/neu membrane receptor immunoreactivity in human breast cancer

Semi‐automated imaging system to quantitate Her‐2/neu membrane receptor immunoreactivity in human breast cancer

A Multi-Agent Expert System for Steel Grade Classification Using Adaptive Neuro-fuzzy Systems

An Adaptive Color Image Segmentation

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