Computer aided erosions and osteophytes detection based on hand radiographs. Issue 7 (July 2015)

• Record Type: Journal Article
• Authors: Zieliński, Bartosz; Skomorowski, Marek; Wojciechowski, Wadim; Korkosz, Mariusz; Sprężak, Kamila
• Is Part Of: Pattern recognition. Volume 48:Issue 7(2015:Jul.)
• Page Start: 2304
• Page End: 2317
• Publication Date: 2015-07
• Subjects: Medical imaging -- Radiographs -- Computer aided rheumatoid diagnosis -- Erosions -- Osteophytes -- Image processing -- Image analysis -- Syntactic pattern recognition; Pattern perception -- Periodicals; Perception des structures -- Périodiques; Patroonherkenning; 006.4
• DOI: 10.1016/j.patcog.2015.01.018
• Languages: English
• ISSNs: 0031-3203

Abstract

Abstract: In this paper we present a computer system to detect erosions and osteophytes from hand radiographs, the most common symptoms of rheumatic diseases. The designed, implemented and verified algorithm uses techniques of image processing, image analysis and pattern recognition. In the stages of image processing and image analysis, the locations of metacarpal bones, the outlines of finger bones, the locations and outlines of joints and finally the borders of joint surfaces are identified. In the pattern recognition stage, a shape description language is used for each border of the joint surface to detect the locations of erosions and osteophytes on hand radiographs. The presented algorithm expands on those known from the literature, because besides erosions it also detects osteophytes. Moreover, in contrast to previous systems, it analyses proximal interphalangeal joints and distal interphalangeal joints. The obtained results are satisfactory and very promising. The joints are successfully located in 98.3% of cases. The average mean distance between the borders pointed out by radiologists and obtained from the system varies between 0.094 mm and 0.157 mm, while the sensitivity and the specificity equal around 70% in most of the cases. Therefore, it can become a basis for the diagnosis of certain diseases. Abstract : Graphical abstract: Abstract : Highlights: We introduce a new algorithm of local segmentation based on a region growing. We define a new shape description language for syntactic pattern recognition. We present a multi-stage algorithm, which expands on those created so far. We designed, implemented and tested an automatic computer system which detects erosions and osteophytes (the system operator has to only load the image and mark the area of the analysis).