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Medical X-ray Image Hierarchical Classification Using a Merging and Splitting Scheme in Feature Space.

Fesharaki NJ, Pourghassem H - J Med Signals Sens (2013)

Bottom Line: Ultimately, in the last levels, this procedure is also continued forming all the classes, separately.In other words, according to the complexity and inter-class distance of each class, a sub-space of the feature space is selected in each level and then a supervised merging and splitting scheme is applied to form the hierarchical classification.The proposed structure is evaluated on a database consisting of 2158 medical X-ray images of 18 classes (IMAGECLEF 2005 database) and accuracy rate of 93.6% in the last level of the hierarchical structure for an 18-class classification problem is obtained.

View Article: PubMed Central - PubMed

Affiliation: Department of Electrical Engineering, Najafabad Branch, Islamic Azad University, Isfahan, Iran.

ABSTRACT
Due to the daily mass production and the widespread variation of medical X-ray images, it is necessary to classify these for searching and retrieving proposes, especially for content-based medical image retrieval systems. In this paper, a medical X-ray image hierarchical classification structure based on a novel merging and splitting scheme and using shape and texture features is proposed. In the first level of the proposed structure, to improve the classification performance, similar classes with regard to shape contents are grouped based on merging measures and shape features into the general overlapped classes. In the next levels of this structure, the overlapped classes split in smaller classes based on the classification performance of combination of shape and texture features or texture features only. Ultimately, in the last levels, this procedure is also continued forming all the classes, separately. Moreover, to optimize the feature vector in the proposed structure, we use orthogonal forward selection algorithm according to Mahalanobis class separability measure as a feature selection and reduction algorithm. In other words, according to the complexity and inter-class distance of each class, a sub-space of the feature space is selected in each level and then a supervised merging and splitting scheme is applied to form the hierarchical classification. The proposed structure is evaluated on a database consisting of 2158 medical X-ray images of 18 classes (IMAGECLEF 2005 database) and accuracy rate of 93.6% in the last level of the hierarchical structure for an 18-class classification problem is obtained.

No MeSH data available.


(a) The main image, (b) the result of contrast increase and noise reduction procedure (processed image)
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Figure 1: (a) The main image, (b) the result of contrast increase and noise reduction procedure (processed image)

Mentions: A combination of techniques for reducing noise and improving contrast is applied for improving the quality of images. High frequency noise is among the characteristics of X-ray images that often low-pass filters are used for eliminating it to remain the main image data by elimination of the high frequencies. Hence, by applying a median filter for the first step, the noise resulted from digital imaging system is reduced. The median filter for elimination of noise in 2-dimensional signals is a very suitable method. To improve the contrast and uniform the overall intensity distribution, the intensity of each pixel is determined through the intensity of pixels in the neighbor of the respective pixel (3 × 3 square window). Furthermore, the grey level of all the image pixels is adjusted again by a way that the area between 0 and 255 is filled.[14] Later on, by employing tools such as adaptive histogram equalization, increasing the contrast would be possible on small areas of the image. For this purpose, first a window with definite dimensions is considered and slipped over the whole image. At each stage, the histogram of the pixels under the window is equalized and the pixels with new values are replaced with them. Equalizing the histogram could be performed in three ways: (1) Uniform distribution, (2) Rayleigh distribution and (3) exponential distribution. The best result in the present research is obtained by the respective tools and using the exponential distribution. It is important to note that equalizing of the histogram, although provides improvement of the image contrast with no destructive effects on the areas with higher contrast,[18] but it increases the noise on the image. As a result, after equalizing the histogram, a median filter (with 5 × 5 square windows) is used again on the image to reduce the noise [Figure 1].


Medical X-ray Image Hierarchical Classification Using a Merging and Splitting Scheme in Feature Space.

Fesharaki NJ, Pourghassem H - J Med Signals Sens (2013)

(a) The main image, (b) the result of contrast increase and noise reduction procedure (processed image)
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3959005&req=5

Figure 1: (a) The main image, (b) the result of contrast increase and noise reduction procedure (processed image)
Mentions: A combination of techniques for reducing noise and improving contrast is applied for improving the quality of images. High frequency noise is among the characteristics of X-ray images that often low-pass filters are used for eliminating it to remain the main image data by elimination of the high frequencies. Hence, by applying a median filter for the first step, the noise resulted from digital imaging system is reduced. The median filter for elimination of noise in 2-dimensional signals is a very suitable method. To improve the contrast and uniform the overall intensity distribution, the intensity of each pixel is determined through the intensity of pixels in the neighbor of the respective pixel (3 × 3 square window). Furthermore, the grey level of all the image pixels is adjusted again by a way that the area between 0 and 255 is filled.[14] Later on, by employing tools such as adaptive histogram equalization, increasing the contrast would be possible on small areas of the image. For this purpose, first a window with definite dimensions is considered and slipped over the whole image. At each stage, the histogram of the pixels under the window is equalized and the pixels with new values are replaced with them. Equalizing the histogram could be performed in three ways: (1) Uniform distribution, (2) Rayleigh distribution and (3) exponential distribution. The best result in the present research is obtained by the respective tools and using the exponential distribution. It is important to note that equalizing of the histogram, although provides improvement of the image contrast with no destructive effects on the areas with higher contrast,[18] but it increases the noise on the image. As a result, after equalizing the histogram, a median filter (with 5 × 5 square windows) is used again on the image to reduce the noise [Figure 1].

Bottom Line: Ultimately, in the last levels, this procedure is also continued forming all the classes, separately.In other words, according to the complexity and inter-class distance of each class, a sub-space of the feature space is selected in each level and then a supervised merging and splitting scheme is applied to form the hierarchical classification.The proposed structure is evaluated on a database consisting of 2158 medical X-ray images of 18 classes (IMAGECLEF 2005 database) and accuracy rate of 93.6% in the last level of the hierarchical structure for an 18-class classification problem is obtained.

View Article: PubMed Central - PubMed

Affiliation: Department of Electrical Engineering, Najafabad Branch, Islamic Azad University, Isfahan, Iran.

ABSTRACT
Due to the daily mass production and the widespread variation of medical X-ray images, it is necessary to classify these for searching and retrieving proposes, especially for content-based medical image retrieval systems. In this paper, a medical X-ray image hierarchical classification structure based on a novel merging and splitting scheme and using shape and texture features is proposed. In the first level of the proposed structure, to improve the classification performance, similar classes with regard to shape contents are grouped based on merging measures and shape features into the general overlapped classes. In the next levels of this structure, the overlapped classes split in smaller classes based on the classification performance of combination of shape and texture features or texture features only. Ultimately, in the last levels, this procedure is also continued forming all the classes, separately. Moreover, to optimize the feature vector in the proposed structure, we use orthogonal forward selection algorithm according to Mahalanobis class separability measure as a feature selection and reduction algorithm. In other words, according to the complexity and inter-class distance of each class, a sub-space of the feature space is selected in each level and then a supervised merging and splitting scheme is applied to form the hierarchical classification. The proposed structure is evaluated on a database consisting of 2158 medical X-ray images of 18 classes (IMAGECLEF 2005 database) and accuracy rate of 93.6% in the last level of the hierarchical structure for an 18-class classification problem is obtained.

No MeSH data available.