PRoSPer: Perceptual similarity queries in medical CBIR systems through user profiles

Bugatti, Pedro Henrique; Kaster, Daniel S.; Ponciano-Silva, Marcelo; Traina, Caetano; Azevedo-Marques, Paulo Mazzoncini de; Traina, Agma J. M.

doi:10.1016/j.compbiomed.2013.11.015

Cited by 16 publications

(16 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Example 4 depicts how the imageSJBindex looks like conceptually for a sample imageDW. [3,5], and [6,8]. Also, we organize all perceptual layers using the same binning strategy, although different binning strategies could be used to index each perceptual layer.…”

Section: Data Structure Of the Imagesjbindexmentioning

confidence: 99%

See 1 more Smart Citation

A similarity-based data warehousing environment for medical images

Teixeira

Annibal

Felipe

et al. 2015

Computers in Biology and Medicine

View full text Add to dashboard Cite

A core issue of the decision-making process in the medical field is to support the execution of analytical (OLAP) similarity queries over images in data warehousing environments. In this paper, we focus on this issue. We propose imageDWE, a non-conventional data warehousing environment that enables the storage of intrinsic features taken from medical images in a data warehouse and supports OLAP similarity queries over them. To comply with this goal, we introduce the concept of perceptual layer, which is an abstraction used to represent an image dataset according to a given feature descriptor in order to enable similarity search. Based on this concept, we propose the imageDW, an extended data warehouse with dimension tables specifically designed to support one or more perceptual layers. We also detail how to build an imageDW and how to load image data into it. Furthermore, we show how to process OLAP similarity queries composed of a conventional predicate and a similarity search predicate that encompasses the specification of one or more perceptual layers. Moreover, we introduce an index technique to improve the OLAP query processing over images. We carried out performance tests over a data warehouse environment that consolidated medical images from exams of several modalities. The results demonstrated the feasibility and efficiency of our proposed imageDWE to manage images and to process OLAP similarity queries. The results also demonstrated that the use of the proposed index technique guaranteed a great improvement in query processing.

show abstract

Section: Data Structure Of the Imagesjbindexmentioning

confidence: 99%

“…To be computationally analyzed, images should be pre-processed using through feature extractors, which are responsible for generating feature vectors that describe their intrinsic characteristics [8]. This process is detailed as follows.…”

Section: Similarity Searchmentioning

confidence: 99%

A similarity-based data warehousing environment for medical images

Teixeira

Annibal

Felipe

et al. 2015

Computers in Biology and Medicine

View full text Add to dashboard Cite

show abstract

“…A shape is the form of an object or its external surface as opposed to other properties such as color, texture or material composition. Bugatti et al (2014) present a novel approach to perform similarity queries over medical images, maintaining the semantics of a given query posted by the user. And also present a highly effective strategy to survey user profiles, taking advantage of such labeling to implicitly gather the user perceptual similarity.…”

Section: Introductionmentioning

confidence: 99%

“…Experiments on medical images show that the method is effective and can improve the decision making process during analysis. [5] Verma et al (2015) present a new image retrieval technique; local extrema co-occurrence patterns (LECoP) using the HSV color space. HSV color space is used in this method to utilize the color, intensity and brightness of images.…”

Section: Introductionmentioning

confidence: 99%

An Efficient Technique for Image Retrieval from the Large Database on the Basis of Color and Texture

Jain¹,

Singh²

2016

IJCA

View full text Add to dashboard Cite

Now a day's development of multimedia technology, the possibilities of utility of large databases is rapidly increasing. To handle its management and retrieval CBIR is the best and effective method. CBIR technique uses the visual contents like as color, shape and texture that are called features, to searching, browsing, and navigation of query images for large image databases. Color is the visual perceptual property corresponding in humans to the categories called red, blue and yellow etc. Texture is the image and especially physical quality of a surface. Texture is the characteristic structure of the interwoven or intertwined outfit, strands or the like that make up a textile fabric. In this paper we present utility of CBIR system with color and texture features. And we design a color filter with the help of extract the red channel, green channel and blue channel from the original image. After it we find a texture of all channels with the help of statistical method. The combination of texture of red channel, green channel and blue channel we create a feature vector for all images of the database. Experimental results are shows the average accuracy, average precision rate and average retrieval rate. That is better than other existing method.

show abstract

“…Automatic illustration identification has been explored for illustrations in medical publications which may assist a clinician in determining the usefulness of a particular publication for patient monitoring and treatment [1] 2) Hough transform shape detection for region of interest determination and segmentation (has been used for lung images) [10]; 3) color analysis of stains for region of interest labeling (has been used for malaria cell images) [13]; 4) connecting the user and the database through a search engine with a feedback neural network architecture [14]; 5) query system modeling human interaction [15]; 6) Big Data use with query forms [16]; 7) use of image "key points" to identify salient parts of an image [17]; 7) combining image and text information for matrix similarity assessment [18]; 8) threedimensional image analysis [8]; 9) latent topic models for computing image similarity [19]; 10) statistical model-based image feature extraction using the wavelet domain and a Kullback divergence-based similarity measure for CBIR [21]; and 11) localized texture characterizations for CBIR for remote sensing applications [22].…”

Section: Introductionmentioning

confidence: 99%

Features Advances to Automatically Find Images for Application to Clinical Decision Support

Stanley¹

2016

MRAJ

View full text Add to dashboard Cite

Filtering through ever increasing sources of information to find relevant information for clinical decisions is a challenging task for clinicians. In biomedical publications, there are a variety of items that can provide evidence to aid the decision making process. One example is illustration image analysis and classification, which has been used to characterize and distinguish specific image modalities; this capability in turn has been used to assist in the evidence gathering process. This paper examines clinical decision support applications and extends previous research for illustration modality discrimination analysis.Specifically, global, HSV histogram-based, and Gabor filter-based features are compared to histogram-based features for modality classification on a set of 12,056 images from 2004-2006 biomedical publication issues of Radiology and RadioGraphics that were manually annotated by modality (radiological, photo, etc.). Using a nearest neighbor classifier, average modality discrimination results were obtained as high as 99.98% using correlated features computed from Gabor filter spectral coefficients. These experimental results indicate that image features, particularly correlationbased features, can provide modality discrimination useful for clinical decision support applications.

show abstract

PRoSPer: Perceptual similarity queries in medical CBIR systems through user profiles

Cited by 16 publications

References 16 publications

A similarity-based data warehousing environment for medical images

A similarity-based data warehousing environment for medical images

An Efficient Technique for Image Retrieval from the Large Database on the Basis of Color and Texture

Features Advances to Automatically Find Images for Application to Clinical Decision Support

Contact Info

Product

Resources

About