A relative entropy-based approach to image thresholding

Chang, Chein‐I; Chen, Kebo; Wang, Jianwei; Althouse, Mark L.G.

doi:10.1016/0031-3203(94)90011-6

Cited by 131 publications

(84 citation statements)

References 10 publications

(6 reference statements)

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“…Formally, the closer the agreement map to the ground data, the lower the cross-entropy and the higher the map similarity accuracy. For a detailed discussion of cross-entropy see Foody (1995) and Chang et al (1994). As a single index value, cross entropy can be readily interpreted to evaluate how well the fuzzy agreement and disagreement patterns represent change on the ground.…”

Section: Resultsmentioning

confidence: 99%

Hierarchical fuzzy pattern matching for the regional comparison of land use maps

Power

Simms

White

2001

International Journal of Geographical Information Science

127

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Abstract. The evaluation of the spatial similarities and land use change between two raster maps is traditionally based on pixel-by-pixel comparison techniques. However, a pixel-by-pixel comparison can register a small displacement in pixels as land use disagreement even though the land use patterns may be essentially the same. The techniques of unique polygons mapping and hierarchical fuzzy pattern matching, where the maps are compared on both a local and global level, are combined to provide a more robust alternative approach. Local matchings determine the degree of containment of each unique polygon in the template map in terms of fuzzy areal intersections. Formally, the local agreement values are based on polygon property containments and are calculated from a fuzzy logical Max-Min compositional algorithm. A global agreement value is derived by the fuzzy summation of the local matchings. The uses of these basic methods are discussed and further re nements and modelling possibilities are outlined.

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Section: Resultsmentioning

confidence: 99%

Hierarchical fuzzy pattern matching for the regional comparison of land use maps

Power

Simms

White

2001

International Journal of Geographical Information Science

127

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“…The concept of entropy becomes increasingly important in image processing, when an image can be interpreted as an information source with the probability law given by its image histogram [52][53][54][55][56].…”

Section: Related Workmentioning

confidence: 99%

A generalized entropy-based two-phase threshold algorithm for noisy medical image edge detection

Elaraby

Moratal

2017

Scientia Iranica

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Abstract. Edge detection in medical imaging is a signi cant task for object recognition of human organs and is considered a pre-processing step in medical image segmentation and reconstruction. This article proposes an e cient approach based on generalized Hill entropy to nd a good solution for detecting edges under noisy conditions in medical images. The proposed algorithm uses a two-phase thresholding: rstly, a global threshold calculated by means of generalized Hill entropy is used to separate the image into object and background. Afterwards, a local threshold value is determined for each part of the image. The nal edge map image is a combination of these two separate images based on the three calculated thresholds. The performance of the proposed algorithm is compared to Canny and Tsallis entropy using sets of medical images corrupted by various types of noise. We used Pratt's Figure Of Merit (PFOM) as a quantitative measure for an objective comparison. Experimental results indicated that the proposed algorithm displayed superior noise resilience and better edge detection than Canny and Tsallis entropy methods for the four di erent types of noise analyzed, and thus it can be considered as a very interesting edge detection algorithm on noisy medical images.

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“…Many thresholding techniques have been proposed in the literature [47]. In this module, the thresholding methods used in this step are entropy-based thresholding methods [48][49][50], which were shown to perform better in terms of capturing the characteristics of MCCs than other popular thresholding methods such as Otsu's method [51].…”

Section: Segmentation Of Mccs From the Background By Entropy-based Thmentioning

confidence: 99%

“…A widely used co-occurrence matrix is an asymmetric matrix, which only considers the gray level transitions between two adjacent pixels. More specifically, let t ij be the (i, j )th element of the co-occurrence matrix W. Following the definition given in [48,49],…”

Section: Segmentation Of Mccs From the Background By Entropy-based Thmentioning

confidence: 99%

“…So, in order to allow radiologists to make various comparison among different thresholding methods, this module includes three entropic thresholding methods (LE,JE,GE), three relative entropic thresholding methods (LRE,JRE,GRE), one popular threshold method (Otsu's method [51]) plus a manual threshold adjustment (that allows radiologists to be able to manually adjust the threshold value themselves). For detailed treatments of these methods we refer to references [48][49][50].…”

Section: H Ge (T)=h Le (T)+h Je (T)mentioning

confidence: 99%

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A computer-aided design mammography screening system for detection and classification of microcalcifications

Lee¹,

Lo²,

Wang³

et al. 2000

International Journal of Medical Informatics

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This paper presents a prototype of a computer-aided design (CAD) diagnostic system for mammography screening to automatically detect and classify microcalcifications (MCCs) in mammograms. It comprises four modules. The first module, called the Mammogram Preprocessing Module, inputs and digitizes mammograms into 8-bit images of size 2048× 2048, extracts the breast region from the background, enhances the extracted breast and stores the processed mammograms in a data base. Since only clustered MCCs are of interest in providing a sign of breast cancer, the second module, called the MCCs Finder Module, finds and locates suspicious areas of clustered MCCs, called regions of interest (ROIs). The third module, called the MCCs Detection Module, is a real time computer automated MCCs detection system that takes as inputs the ROIs provided by the MCCs Finder Module. It uses two different window sizes to automatically extract the microcalcifications from the ROIs. It begins with a large window of size 64 × 64 to quickly screen mammograms to find large calcified areas, this is followed by a smaller window of size 8 ×8 to extract tiny, isolated microcalcifications. Finally, the fourth module, called the MCCs Classification Module, classifies the detected clustered microcalcifications into five categories according to BI-RADS (Breast Imaging Reporting and Data System) format recommended by the American College of Radiology. One advantage of the designed system is that each module is a separate component that can be individually upgraded to improve the whole system. Despite that it is still is a prototype system a preliminary clinical evaluation at TaiChung Veterans General Hospital (TCVGH) has shown that the system is very flexible and can be integrated with the existing Picture Archiving and Communications System (PACS) currently implemented in the Department of Radiology at TCVGH.

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A relative entropy-based approach to image thresholding

Cited by 131 publications

References 10 publications

Hierarchical fuzzy pattern matching for the regional comparison of land use maps

Hierarchical fuzzy pattern matching for the regional comparison of land use maps

A generalized entropy-based two-phase threshold algorithm for noisy medical image edge detection

A computer-aided design mammography screening system for detection and classification of microcalcifications

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