M
Musa H. Asyali
Researcher at Zirve University
Publications - 54
Citations - 1717
Musa H. Asyali is an academic researcher from Zirve University. The author has contributed to research in topics: Image segmentation & Laguerre polynomials. The author has an hindex of 20, co-authored 54 publications receiving 1554 citations. Previous affiliations of Musa H. Asyali include Abdullah Gül University & Ege University.
Papers
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Journal ArticleDOI
Sample size requirements for the design of reliability study: review and new results
TL;DR: The reliability of continuous or binary outcome measures is usually assessed by estimation of the intraclass correlation coefficient (ICC), and the optimal allocation for the number of subjects k and thenumber of repeated measurements n that minimize the variance of the estimated ICC is discussed.
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Sleep stage and obstructive apneaic epoch classification using single-lead ECG
TL;DR: In this article, the authors investigated the feasibility of automatic classification of sleep stages and obstructive apneaic epochs using only the features derived from a single-lead electrocardiography (ECG) signal.
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Gene Expression Profile Classification: A Review
TL;DR: This review attempted to present a unified approach that considers both class-prediction and class-discovery, and discussed important issues such as preprocessing of gene expression data, curse of dimensionality, feature extraction/selection, and measuring or estimating classifier performance.
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Feedback Network Controls Photoreceptor Output at the Layer of First Visual Synapses in Drosophila
Lei Zheng,Gonzalo G. de Polavieja,Verena Wolfram,Musa H. Asyali,Roger C. Hardie,Mikko Juusola +5 more
TL;DR: It is shown that the feedback synapses form a negative feedback loop that controls the speed and amplitude of photoreceptor responses and hence the quality of the transmitted signals.
Book
Image Processing with Matlab: Applications in Medicine and Biology
TL;DR: Medical Imaging Systems Fundamental Tools for Image Processing and Analysis Probability Theory for Stochastic Modeling of Images Two-Dimensional Fourier Transform Nonlinear Diffusion Filtering Intensity-Based Image Segmentation image segmentation by Markov Random Field Modeling Deformable Models Image Analysis.