title: Interactive knee cartilage extraction using efficient segmentation software: data from the osteoarthritis initiative.
abstract: In medical image segmentation, manual segmentation is considered both labor- and time-intensive while automated segmentation often fails to segment anatomically intricate structure accordingly. Interactive segmentation can tackle shortcomings reported by previous segmentation approaches through user intervention. To better reflect user intention, development of suitable editing functions is critical. In this paper, we propose an interactive knee cartilage extraction software that covers three important features: intuitiveness, speed, and convenience. The segmentation is performed using multi-label random walks algorithm. Our segmentation software is simple to use, intuitive to normal and osteoarthritic image segmentation and efficient using only two third of manual segmentation's time. Future works will extend this software to three dimensional segmentation and quantitative analysis.
authors.affiliation: Department of Biotechnology and Medical Engineering, Faculty of Biosciences and Medical Engineering, Universiti Teknologi Malaysia, 81310 Skudai, Johor, Malaysia.
authors.forename: Hong-Seng
authors.initials: HS
authors.lastname: Gan
authors.affiliation: Department of Biotechnology and Medical Engineering, Faculty of Biosciences and Medical Engineering, Universiti Teknologi Malaysia, 81310 Skudai, Johor, Malaysia.
authors.forename: Tian-Swee
authors.initials: TS
authors.lastname: Tan
authors.affiliation: Department of Control Engineering and Mechatronic Engineering, Faculty of Electrical Engineering, Universiti Teknologi Malaysia, 81310 Skudai, Johor, Malaysia.
authors.forename: Liang-Xuan
authors.initials: LX
authors.lastname: Wong
authors.affiliation: Department of Control Engineering and Mechatronic Engineering, Faculty of Electrical Engineering, Universiti Teknologi Malaysia, 81310 Skudai, Johor, Malaysia.
authors.forename: Weng-Kit
authors.initials: WK
authors.lastname: Tham
authors.affiliation: Department of Radiology, School of Medical Sciences, Universiti Sains Malaysia, 16150 Kubang Kerian, Kelantan, Malaysia.
authors.forename: Khairil Amir
authors.initials: KA
authors.lastname: Sayuti
authors.affiliation: Department of Radiology, School of Medical Sciences, Universiti Sains Malaysia, 16150 Kubang Kerian, Kelantan, Malaysia.
authors.forename: Ahmad Helmy
authors.initials: AH
authors.lastname: Abdul Karim
authors.affiliation: Department of Clinical Sciences, Faculty of Biosciences and Medical Engineering, Universiti Teknologi Malaysia, 81310 Skudai, Johor, Malaysia.
authors.forename: Mohammed Rafiq
authors.initials: MR
authors.lastname: bin Abdul Kadir
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D002358:Cartilage, Articular / Q000473:pathology*; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D015588:Observer Variation; D020370:Osteoarthritis, Knee / Q000473:pathology*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D014584:User-Computer Interface
keywords: Interactive segmentation; Knee cartilage; Magnetic resonance image; Random walks; User interface