This paper summarises the results of the Sclera Segmentation and Eye Recognition Benchmarking Competition (SSERBC 2017). It was organised in the context of the International Joint Conference on Biometrics (IJCB 2017). The aim of this competition was to record the recent developments in sclera segmentation and eye recognition in the visible spectrum (using iris, sclera and peri-ocular, and their fusion), and also to gain the attention of researchers on this subject. In this regard, we have used the Multi-Angle Sclera Dataset (MASD version 1). It is comprised of 2624 images taken from both the eyes of 82 identities. Therefore, it consists of images of 164 (82*2) eyes. A manual segmentation mask of these images was created to baseline both tasks. Precision and recall based statistical measures were employed to evaluate the effectiveness of the segmentation and the ranks of the segmentation task. Recognition accuracy measure has been employed to measure the recognition task. Manually segmented sclera, iris and periocular regions were used in the recognition task. Sixteen teams registered for the competition, and among them, six teams submitted their algorithms or systems for the segmentation task and two of them submitted their recognition algorithm or systems. The results produced by these algorithms or systems reflect current developments in the literature of sclera segmentation and eye recognition, employing cutting edge techniques. The MASD version 1 dataset with some of the ground truth will be freely available for research purposes. The success of the competition also demonstrates the recent interests of researchers from academia as well as industry on this subject.

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This paper presents a modified intuitionistic fuzzy clustering (IFCM) algorithm for medical image segmentation. IFCM is a variant of the conventional fuzzy C-means (FCM) based on intuitionistic fuzzy set (IFS) theory. Unlike FCM, IFCM considers both membership and nonmembership values. The existing IFCM method uses Sugeno’s and Yager’s IFS generators to compute nonmembership value. But for certain parameters, IFS constructed using above complement generators does not satisfy the elementary condition of intuitionism. To overcome this problem, this paper adopts a new IFS generator. Further, Hausdorff distance is used as distance metric to calculate the distance between cluster center and pixel. Extensive experimentations are carried out on standard datasets like brain, lungs, liver and breast images. This paper compares the proposed method with other IFS based methods. The proposed algorithm satisfies the elementary condition of intuitionism. Further, this algorithm outperforms other methods with the use of various cluster validity functions.

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Medical image segmentation is a fundamental preprocessing step in most systems that supports diagnosis or planning of surgical operations. The traditional Fuzzy c means clustering algorithm performs well in the absence of noise. Traditional FCM leads to its non robust result mainly due to 1. Not utilizing the spatial information in the image. 2. Use of Euclidean distance. These limitations can be addressed by using robust spatial kernel FCM (RSKFCM). RSKFCM consider the spatial information and uses Gaussian kernel function to calculate the distance between the center and data points. Though RSKFCM gives good result, the main drawback behind this method is the inability of generating global minima for the objective function. To improve the efficiency of RSKFCM method, in this paper we proposed the genetic algorithm based RSKFCM. By using the genetic algorithm, RSKFCM initializes the cluster centers and reaches the global minima of the objective function. Experimentation is carried out on the standard brain image dataset. Experimental result reveals that the proposed genetic algorithm based RSKFCM outperforms other FCM methods with the use of various cluster validity functions.