Segmentation of pictures into regions with a tile-by-tile method

Browning, Jason D.; Tanimoto, Steven L.

doi:10.1016/0031-3203(82)90055-3

Cited by 30 publications

(11 citation statements)

References 8 publications

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“…First, it decreases space complexity for feature extraction [23]. Second, a previous study has demonstrated that when each image captures many cells, dividing the image into blocks to extract local features can, in some cases, improve the results in comparison with other approaches using global features [24].…”

Section: A Image Divisionmentioning

confidence: 99%

Histology Image Classification Using Supervised Classification and Multimodal Fusion

Meng

Lin

Shyu

et al. 2010

2010 IEEE International Symposium on Multimedia

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Abstract-The fast development of microscopy imaging techniques nowadays promotes the generation of a large amount of data. These data are very crucial not only for theoretical biomedical research but also for clinical usage. In order to decrease the inter-intra observer variability and save the human effort on labeling and classifying these images, a lot of research efforts have been devoted to the development of algorithms for biomedical images. Among such efforts, histology image classification is one of the most important areas due to its broad applications in pathological diagnosis such as cancer diagnosis. To improve classification accuracy, most of the previous work focuses on extracting more features and building algorithms for a specific task. This paper proposes a framework based on the novel and robust Collateral Representative Subspace Projection Modeling (C-RSPM) supervised classification model for general histology image classification. In the proposed framework, a cell image is first divided into 25 blocks to reduce the spatial complexity of computation, and one C-RSPM model is built on each block set which contains blocks in the same location from different images. For each testing image, our proposed framework first classifies each of its blocks using the C-RSPM classification model built for that block set, and then applies a multimodal late fusion algorithm with a weighted majority voting strategy to decide the final class label of the whole image. Experimenting using three-fold cross validation with three benchmark histology data sets shows that the proposed framework outperforms other well-known classifiers in the comparison and gives better results than the highest accuracy reported previously.

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Section: A Image Divisionmentioning

confidence: 99%

Histology Image Classification Using Supervised Classification and Multimodal Fusion

Meng

Lin

Shyu

et al. 2010

2010 IEEE International Symposium on Multimedia

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“…Nagin [38,36] provides a good discussion of the issues surrounding global versus local context in region segmentation, while Rosenfeld and Kak [53] contains good descriptions of many of the region-segmentation techniques commonly used. Two methods that have been used to overcome the problems of global technique are localization of a global method to arbitrary subimages [10,7] and recursive application of the global method to increasingly fine-grained regions [40,41]. Histograms provide a global view of image data: but spatial context is lost.…”

Section: Region Segmentation: Local Versus Global Considerationsmentioning

confidence: 99%

“…The reader is referred to [48] and [22] for a discussion of the relative merits of recursive region segmentation and regular decompositioiL as localization methods. Also, we should note that achieving locality by regular decomposition has been incorporated into other region-segmentation algorithms [10,7,9].…”

Section: Region Segmentation: Local Versus Global Considerationsmentioning

confidence: 99%

Segmenting images using localized histograms and region merging

Beveridge

Griffith

Kohler

et al. 1989

Int J Comput Vision

136

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A working system for segmenting images of complex scenes is presented. The system integrates techniques that have evolved out of many years of research in low-level image segmentation at the University of Massachusetts and elsewhere. This paper documents the result of this historical evolution. Segmentations produced by the system are used extensively in related image interpretation research.The system first produces segmentations based upon an analysis of spatially localized feature histograms. These initial segmentations are then simplified using a region merging algorithm. Parameter selection for the local histogram segmentation algorithm is facilitated by mapping the multidimensional parameter space to a one-dimensional parameter which regulates region fragmentation. An extension of this algorithm to multiple features is also presented. Experience with roughly 100 images from different domains has shown the system to be robust and effective. Samples of these results are included.

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“…The scheme described in [9] uses mapping tables, but the tables are passed from processor to processor in a cyclic manner, requiring a number of communication steps that is equal to the number of processors used. A notion similar to the mapping tables is used in [8], in the context of image segmentation. The algorithm described in this paper partitions the grid into squares rather than into strips.…”

Section: Discussionmentioning

confidence: 99%