One dimensional local binary pattern for bone texture characterization

• Published in: Pattern analysis and applications : PAA Vol. 17; no. 1; pp. 179 - 193
• Main Authors: Houam, Lotfi, Hafiane, Adel, Boukrouche, Abdelhani, Lespessailles, Eric, Jennane, Rachid
• Format: Journal Article
• Language: English
• Published: London Springer London 01.02.2014; Springer; Springer Verlag
• Subjects: Applied sciences; Artificial intelligence; Biological and medical sciences; Computer Science; Computer science; control theory; systems; Engineering Sciences; Exact sciences and technology; Industrial and Commercial Application; Investigative techniques, diagnostic techniques (general aspects); Medical sciences; Osteoarticular system. Muscles; Pathology. Cytology. Biochemistry. Spectrometry. Miscellaneous investigative techniques; Pattern Recognition; Pattern recognition. Digital image processing. Computational geometry; Signal and Image processing; Local binary pattern; Osteoporosis; Texture characterization; Bone; Computer vision; Texture analysis; Texturation; X ray radiography; Pattern recognition; Similarity solution; Texture; Osteoarticular system; Microarchitecture; Image projection; Classification; Medical application; Local binary pattern Bone
• ISSN: 1433-7541; 1433-755X
• DOI: 10.1007/s10044-012-0288-4

The evaluation of osteoporotic disease from X-ray images presents a major challenge for pattern recognition and medical applications. Textured images from the bone microarchitecture of osteoporotic and healthy subjects show a high degree of similarity, thus drastically increasing the difficulty of classifying such textures. In this paper, we propose a new method to separate osteoporotic cases from healthy controls, using texture analysis. The idea consists in combining global and local information to better capture the image characteristics. Global information is characterized by image projection which conveys information about the global aspect of the texture. Local information is encoded by the local patterns using neighborhood operators. The proposed technique is based on the local binary pattern (LBP) descriptor which has been classically applied on two dimensional (2D) images. Our algorithm is a derived solution for the 1D projected fields of the 2D images. Experiments were conducted on two populations of osteoporotic patients and control subjects. Compared to the classical LBP, the proposed approach yields a better classification rate of the two populations.