Month: January 2015

Abstract

This paper presents a no-reference objective blur metric based on edge model (EMBM) to address the image blur assessment problem. A parametric edge model is incorporated to describe and detect edges, which can offer simultaneous width and contrast estimation for each edge pixel. With the pixel-adaptive width and contrast estimations, the probability of detecting blur at edge pixels can be determined. Also, unlike previous work, we advocate using only the salient edge pixels to simulate the blur assessment in Human Visual System (HVS). Finally, the blur metric is obtained by cumulating the probability of blur detection. Various images with different blur distortions are tested to demonstrate the effectiveness of the proposed metric.
 
 

Abstract

The commercial humeral implants based on the Western population are currently not entirely compatible with Asian patients, due to differences in bone size, shape and structure. Surgeons may have to compromise or use different implants that are less conforming, which may cause complications of as well as inconvenience to the implant position. The construction of Asian humerus atlases of different clusters has therefore been proposed to eradicate this problem and to facilitate planning minimally invasive surgical procedures [6,31]. According to the features of the atlases, new implants could be designed specifically for different patients. Furthermore, an automatic implant selection algorithm has been proposed as well in order to reduce the complications caused by implant and bone mismatch. Prior to the design of the implant, data clustering and extraction of the relevant features were carried out on the datasets of each gender. The fuzzy C-means clustering method is explored in this paper. Besides, two new schemes of implant selection procedures, namely the Procrustes analysis-based scheme and the group average distance-based scheme, were proposed to better search for the matching implants for new coming patients from the database. Both these two algorithms have not been used in this area, while they turn out to have excellent performance in implant selection. Additionally, algorithms to calculate the matching scores between various implants and the patient data are proposed in this paper to assist the implant selection procedure. The results obtained have indicated the feasibility of the proposed development and selection scheme. The 16 sets of male data were divided into two clusters with 8 and 8 subjects, respectively, and the 11 female datasets were also divided into two clusters with 5 and 6 subjects, respectively. Based on the features of each cluster, the implants designed by the proposed algorithm fit very well on their reference humeri and the proposed implant selection procedure allows for a scenario of treating a patient with merely a preoperative anatomical model in order to correctly select the implant that has the best fit. Based on the leave-one-out validation, it can be concluded that both the PA-based method and GAD-based method are able to achieve excellent performance when dealing with the problem of implant selection. The accuracy and average execution time for the PA-based method were 100 % and 0.132 s, respectively, while those of the GAD- based method were 100 % and 0.058 s. Therefore, the GAD-based method outperformed the PA-based method in terms of execution speed. The primary contributions of this paper include the proposal of methods for development of Asian-, gender- and cluster-specific implants based on shape features and selection of the best fit implants for future patients according to their features. To the best of our knowledge, this is the first work that proposes implant design and selection for Asian patients automatically based on features extracted from cluster-specific statistical atlases.

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