Visual organization of hip joint osteoathritis data in low-dimensional biplots

Abstract

An ordinary difficulty in assessing the severity of medical data is the confined ability to structurally organize the ensemble of different cases into semantically relevant classes. A conceivable visualization scheme of the entire set can potentially reveal critical data similarities and help the physicians to ocular estimate their initial diagnosis. In this paper, a novel method for efficient organization of hip joint osteoarthritis data in a lowdimensional space is presented. A collection of 57 radiographic images are utilized and characteristic features are extracted using a standard texture-based methodology. The management of all individual cases is pursued using a well-suited data analysis methodology. The final outcome is a 2-dimensional pointdiagram that visually reflects the intrinsic organization of the ensemble of available data. The proposed framework can be used to detect putative clustering tendencies, examine the presence of data similarities and potentially indicate the existence of possible false alarms in the initial perceptual estimation.

Use of dimensionality reduction for structural mapping of hip joint osteoarthritis data

“…The proposed framework can be used to detect putative clustering tendencies, examine the presence of data similarities and potentially indicate the existence of possible false alarms in the initial diagnosis. Furthermore, the suggested approach may be of value for the grading of the severity of hip OA, in the context of radiographic assessment of the disease [21].…”