Liver segmentation is an important prerequisite for planning of surgical interventions like liver tumor resections. In this paper we propose a method for automated liver segmentation from images that is invariant in provisions of size, shape and intensity values. The system consists of three stages. In the first stage of the computerized system, Preprocessing of an image is done to reduce the n...

Expansion of the neocortex is a hallmark of human evolution. However, determining which adaptive mechanisms facilitated its expansion remains an open question. Here we show, using the gyrencephaly index (GI) and other physiological and life-history data for 102 mammalian species, that gyrencephaly is an ancestral mammalian trait. We find that variation in GI does not evolve linearly across spec...

Instance reduction techniques can improve generalization, reduce storage requirements and execution time of instance-based learning algorithms. This paper presents an instance reduction algorithm called Adaptive Threshold-based Instance Selection Algorithm (ATISA). ATISA aims to preserve important instances based on a selection criterion that uses the distance of each instance to its nearest en...

Due to some reasons, observed images are degraded which are mainly caused by noise. Recently image denoising using the wavelet transform has been attracting much attention. Waveletbased approach provides a particularly useful method for image denoising when the preservation of edges in the scene is of importance because the local adaptivity is based explicitly on the values of the wavelet detai...

Consider the problem of computing the intersection of k sorted sets whose sizes sum to n. In the comparison model, we prove a new lower bound which depends on the non-deterministic complexity of the instance, and implies that the algorithm of Demaine, López-Ortiz and Munro [1] is optimal in this “adaptive” sense (for k much smaller than n). We extend the lower bound and the algorithm to the t-T...

In this paper, we describe the system and methods used for the CLARITECH entries in the TREC–7 Filtering Track. Our main aim was to study algorithms, designs, and parameters for Adaptive Filtering, as this comes closest to actual applications. For efficiency's sake, however, we adapted a system largely geared towards retrieval and introduced a few critical new components. The first of these com...

X iv :1 71 2. 07 50 9v 2 [ cs .I T ] 2 3 D ec 2 01 7 Efficiently Decodable Non-Adaptive Threshold Group Testing Thach V. Bui∗, Minoru Kuribayashi‡, Mahdi Cheraghchi§, and Isao Echizen∗† ∗SOKENDAI (The Graduate University for Advanced Studies), Hayama, Kanagawa, Japan [email protected] ‡Graduate School of Natural Science and Technology, Okayama University, Okayama, Japan [email protected] ...

Introduction: One of the leading causes of death among people is brain tumors. Accurate tumor classification leads to appropriate decision-making and providing the most efficient treatment to the patients. This study aims to optimize of the brain tumor MR images classification accuracy using the optimal threshold, PCA and training Adaptive Neuro Fuzzy Inference System (ANFIS) w...

Shot boundary detection is main step in video management systems like browsing and indexing. In this paper, we shortly describe an earlier proposed shot detection algorithm based on the structural properties of video frames. Two mathematical models for decision making, i.e. for similarity threshold determination are proposed and compared. The first model allows determination of threshold in cas...

Threshold-based fall detection has been widely adopted in conventional fall detection systems. In this paper, we argue that a fixed threshold is not flexible enough for different people. By exploiting the personalised and adaptive threshold, we propose a novel threshold extraction model, which meets being adaptive to detect a fall, while only taking consideration of data from activity of daily ...