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Categorizing biomedicine images using novel image features and sparse coding representation.

Sheng J, Xu S, Luo X - BMC Med Genomics (2013)

Bottom Line: A serial of experimental results are obtained.Different features which include conventional image features and our proposed novel features indicate different categorizing performance, and the results are demonstrated.Compared with conventional image features that do not exploit characteristics regarding text positions and distributions inside images embedded in biomedical publications, our proposed image features coupled with the SR based representation model exhibit superior performance for classifying biomedical images as demonstrated in our comparative benchmark study.

View Article: PubMed Central - HTML - PubMed

ABSTRACT

Background: Images embedded in biomedical publications carry rich information that often concisely summarize key hypotheses adopted, methods employed, or results obtained in a published study. Therefore, they offer valuable clues for understanding main content in a biomedical publication. Prior studies have pointed out the potential of mining images embedded in biomedical publications for automatically understanding and retrieving such images' associated source documents. Within the broad area of biomedical image processing, categorizing biomedical images is a fundamental step for building many advanced image analysis, retrieval, and mining applications. Similar to any automatic categorization effort, discriminative image features can provide the most crucial aid in the process.

Method: We observe that many images embedded in biomedical publications carry versatile annotation text. Based on the locations of and the spatial relationships between these text elements in an image, we thus propose some novel image features for image categorization purpose, which quantitatively characterize the spatial positions and distributions of text elements inside a biomedical image. We further adopt a sparse coding representation (SCR) based technique to categorize images embedded in biomedical publications by leveraging our newly proposed image features.

Results: we randomly selected 990 images of the JPG format for use in our experiments where 310 images were used as training samples and the rest were used as the testing cases. We first segmented 310 sample images following the our proposed procedure. This step produced a total of 1035 sub-images. We then manually labeled all these sub-images according to the two-level hierarchical image taxonomy proposed by 1. Among our annotation results, 316 are microscopy images, 126 are gel electrophoresis images, 135 are line charts, 156 are bar charts, 52 are spot charts, 25 are tables, 70 are flow charts, and the remaining 155 images are of the type "others". A serial of experimental results are obtained. Firstly, each image categorizing results is presented, and next image categorizing performance indexes such as precision, recall, F-score, are all listed. Different features which include conventional image features and our proposed novel features indicate different categorizing performance, and the results are demonstrated. Thirdly, we conduct an accuracy comparison between support vector machine classification method and our proposed sparse representation classification method. At last, our proposed approach is compared with three peer classification method and experimental results verify our impressively improved performance.

Conclusions: Compared with conventional image features that do not exploit characteristics regarding text positions and distributions inside images embedded in biomedical publications, our proposed image features coupled with the SR based representation model exhibit superior performance for classifying biomedical images as demonstrated in our comparative benchmark study.

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Related in: MedlinePlus

Image segmentation result by our implemented method. (a) A sample imagefrom the aritlce [48], (b) sub-images decomposed from the sample image, which consists ofa line chart and a bar chart.
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Figure 2: Image segmentation result by our implemented method. (a) A sample imagefrom the aritlce [48], (b) sub-images decomposed from the sample image, which consists ofa line chart and a bar chart.

Mentions: Algorithm 1 lists the image segmentation procedure used in our work, whose main ideais as follows: We first apply the Gaussian filter function, whose implementation isoffered by the OpenCV 2.2.0 package, to remove local noise in an input imageI. We then convert the image into its binary counterpart representation.After that we scan the whole image I following all the horizontal andvertical scanlines in the image respectively, one scanline at a time. The goal is tofind suitable horizontal and/or vertical scanlines that can segment the image Iinto its constituent panels or sub-images. For each scanline we consider, wecalculate the number of foreground pixels Np in the image thatlie on the line. When the number of Np is larger 5, weempirically regard the line as a candidate image segmentation line. After all thecandidate image segmentation scanlines are detected, we then apply them collectivelyto divide I into multiple sub-regions. At last, we retain those dividedsub-regions whose respective areas are no smaller than 1/20 of the total image area.Figure 2 shows an example image segmentation result generatedby the above procedure.


Categorizing biomedicine images using novel image features and sparse coding representation.

Sheng J, Xu S, Luo X - BMC Med Genomics (2013)

Image segmentation result by our implemented method. (a) A sample imagefrom the aritlce [48], (b) sub-images decomposed from the sample image, which consists ofa line chart and a bar chart.
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4109834&req=5

Figure 2: Image segmentation result by our implemented method. (a) A sample imagefrom the aritlce [48], (b) sub-images decomposed from the sample image, which consists ofa line chart and a bar chart.
Mentions: Algorithm 1 lists the image segmentation procedure used in our work, whose main ideais as follows: We first apply the Gaussian filter function, whose implementation isoffered by the OpenCV 2.2.0 package, to remove local noise in an input imageI. We then convert the image into its binary counterpart representation.After that we scan the whole image I following all the horizontal andvertical scanlines in the image respectively, one scanline at a time. The goal is tofind suitable horizontal and/or vertical scanlines that can segment the image Iinto its constituent panels or sub-images. For each scanline we consider, wecalculate the number of foreground pixels Np in the image thatlie on the line. When the number of Np is larger 5, weempirically regard the line as a candidate image segmentation line. After all thecandidate image segmentation scanlines are detected, we then apply them collectivelyto divide I into multiple sub-regions. At last, we retain those dividedsub-regions whose respective areas are no smaller than 1/20 of the total image area.Figure 2 shows an example image segmentation result generatedby the above procedure.

Bottom Line: A serial of experimental results are obtained.Different features which include conventional image features and our proposed novel features indicate different categorizing performance, and the results are demonstrated.Compared with conventional image features that do not exploit characteristics regarding text positions and distributions inside images embedded in biomedical publications, our proposed image features coupled with the SR based representation model exhibit superior performance for classifying biomedical images as demonstrated in our comparative benchmark study.

View Article: PubMed Central - HTML - PubMed

ABSTRACT

Background: Images embedded in biomedical publications carry rich information that often concisely summarize key hypotheses adopted, methods employed, or results obtained in a published study. Therefore, they offer valuable clues for understanding main content in a biomedical publication. Prior studies have pointed out the potential of mining images embedded in biomedical publications for automatically understanding and retrieving such images' associated source documents. Within the broad area of biomedical image processing, categorizing biomedical images is a fundamental step for building many advanced image analysis, retrieval, and mining applications. Similar to any automatic categorization effort, discriminative image features can provide the most crucial aid in the process.

Method: We observe that many images embedded in biomedical publications carry versatile annotation text. Based on the locations of and the spatial relationships between these text elements in an image, we thus propose some novel image features for image categorization purpose, which quantitatively characterize the spatial positions and distributions of text elements inside a biomedical image. We further adopt a sparse coding representation (SCR) based technique to categorize images embedded in biomedical publications by leveraging our newly proposed image features.

Results: we randomly selected 990 images of the JPG format for use in our experiments where 310 images were used as training samples and the rest were used as the testing cases. We first segmented 310 sample images following the our proposed procedure. This step produced a total of 1035 sub-images. We then manually labeled all these sub-images according to the two-level hierarchical image taxonomy proposed by 1. Among our annotation results, 316 are microscopy images, 126 are gel electrophoresis images, 135 are line charts, 156 are bar charts, 52 are spot charts, 25 are tables, 70 are flow charts, and the remaining 155 images are of the type "others". A serial of experimental results are obtained. Firstly, each image categorizing results is presented, and next image categorizing performance indexes such as precision, recall, F-score, are all listed. Different features which include conventional image features and our proposed novel features indicate different categorizing performance, and the results are demonstrated. Thirdly, we conduct an accuracy comparison between support vector machine classification method and our proposed sparse representation classification method. At last, our proposed approach is compared with three peer classification method and experimental results verify our impressively improved performance.

Conclusions: Compared with conventional image features that do not exploit characteristics regarding text positions and distributions inside images embedded in biomedical publications, our proposed image features coupled with the SR based representation model exhibit superior performance for classifying biomedical images as demonstrated in our comparative benchmark study.

Show MeSH
Related in: MedlinePlus