Content based sub-image retrieval system for high resolution pathology images using salient interest points

Mehta, Neville; Alomari, Raja’ S.; Chaudhary, Vipin

doi:10.1109/iembs.2009.5334811

Cited by 35 publications

(29 citation statements)

References 9 publications

(8 reference statements)

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“…However, their efficiency has been questioned because they need a large number of local patches for representing each image (Nowak et al, 2006). The use of conventional part extraction approaches has been explored in histological (Caicedo and Izquierdo, 2010) and histopathological images Cruz-Roa et al, 2009;Mehta et al, 2009), but with no specific interest on searching visual and structural features.…”

Section: Previous Workmentioning

confidence: 99%

Micro‐structural tissue analysis for automatic histopathological image annotation

Díaz

Romero

2011

Microscopy Res & Technique

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This article presents a new approach for extracting high level semantic concepts from digital histopathological images. This strategy provides not only annotation of several biological concepts, but also a coarse location of these concepts. The proposed approach is composed of five main steps: (1) a stain decomposition stage, which separates the contribution of hematoxylin and eosin dyes, (2) a color standardization that corrects color image differences, (3) a part-based representation, which describes the image in terms of the conditional probability of relevant local patches, selected by their stain contributions, (4) a discriminative classification model, which bridges out the found patterns and the biological concepts, (5) a block-based annotation strategy that identifies the multiple biological concepts within an image. A set of 655 skin images, containing 10 biological concepts of skin tissues were used for assessing the proposed approach, obtaining a sensitivity of 84% and a specificity of 67% when annotating images with multiple concepts.

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Section: Previous Workmentioning

confidence: 99%

Micro‐structural tissue analysis for automatic histopathological image annotation

Díaz

Romero

2011

Microscopy Res & Technique

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“…For the last decade, a few CBIR systems for the microscopic images have been developed for clinical use [6], [21], [22], [7], [16]. Mehta et al designed a region specific retrieval system based on sub-image query search on whole slide images by extracting scale invariant features on the detected points of interests and 80% of match was achieved with the manual search for prostate H&E images [22] in the top five searches.…”

Section: Related Workmentioning

confidence: 99%

“…Mehta et al designed a region specific retrieval system based on sub-image query search on whole slide images by extracting scale invariant features on the detected points of interests and 80% of match was achieved with the manual search for prostate H&E images [22] in the top five searches. In another study, image-level retrieval of four special types of skin cancer [21] was performed by constructing a visual word dictionary under a bag-of-features approach in order to represent a relationship between visual patterns and semantic concepts.…”

Section: Related Workmentioning

confidence: 99%

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Content-Based Microscopic Image Retrieval System for Multi-Image Queries

Akakın

Gürcan

2012

IEEE Trans. Inform. Technol. Biomed.

102

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In this paper, we describe the design and development of a multi-tiered CBIR system for microscopic images utilizing a reference database that contains images of more than one disease. Proposed CBIR system uses a multi-tiered approach to classify and retrieve microscopic images involving their specific subtypes which are mostly difficult to discriminate and classify. This system enables both multi-image query and slide-level image retrieval in order to protect the semantic consistency among the retrieved images. New weighting terms, inspired from information retrieval (IR) theory, are defined for multiple-image query and retrieval. Performance of the system was tested on a dataset including 1666 imaged high power fields (HPF) extracted from 57 Follicular Lymphoma (FL) tissue slides with three subtypes and 44 Neuroblastoma (NB) tissue slides with four subtypes, where each slide is semantically annotated according to their subtypes by expert pathologists. By using leave-one-slide out testing scheme, the multi-image query algorithm with the proposed weighting strategy achieves about 93% and 86% of average classification accuracy at the first rank retrieval, outperforming the image-level retrieval accuracy by about 38 and 26 percentage points, for FL and NB diseases, respectively.

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“…By using color information in different color spaces, the interest points gain distinctiveness and stability. Mehta et al [23] proposed a content-based sub-image retrieval system framework for high resolution digital pathology images. They utilized scale-invariant feature extraction and presented an efficient and robust searching mechanism for indexing the images as well as for query execution of sub-image retrieval.…”

Section: Introductionmentioning

confidence: 99%

Content-based Image Retrieval using Visual Attention Point Features

Wang

Niu

et al. 2014

Fundamenta Informaticae

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One of the challenges in the development of a content-based image indexing and retrieval application is to achieve an efficient and robust indexing scheme. Color is a fundamental image feature used in content-based image retrieval (CBIR) systems. This paper proposes a robust and effective image retrieval scheme, which is based on the weighed color histogram of visual attention points. Firstly, the fully affine invariant visual attention points are extracted from the origin color image by using the Affine-SIFT (scale-invariant feature transform) detector. Secondly, according to the color complexity measure (CCM) theory, the visual weight values for the significant visual attention points are calculated to reflect the image local variation. Then, the weighed color histogram of visual attention points is constructed. Finally, the similarity between color images is computed by using the weighed color histogram of visual attention points. Experimental results show that the proposed image retrieval is not only more accurate and efficient in retrieving the user-interested images, but also yields higher retrieval accuracy than some state-of-the-art image retrieval schemes for various test DBs.

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Content based sub-image retrieval system for high resolution pathology images using salient interest points

Cited by 35 publications

References 9 publications

Micro‐structural tissue analysis for automatic histopathological image annotation

Micro‐structural tissue analysis for automatic histopathological image annotation

Content-Based Microscopic Image Retrieval System for Multi-Image Queries

Content-based Image Retrieval using Visual Attention Point Features

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