Open AccessAN ARTIFICIAL NEURAL PROCESS TO CREATE CONTINUOUS OBJECT BOUNDARIES IN MEDICAL IMAGE ANALYSIS
Author(s) -
Mahinda P. Pathegama,
Özdemir Göl
Publication year - 2014
Publication title -
computing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.184
H-Index - 11
eISSN - 2312-5381
pISSN - 1727-6209
DOI - 10.47839/ijc.3.1.249
Subject(s) - thresholding , artificial intelligence , computer science , pixel , artificial neural network , computer vision , edge detection , image processing , process (computing) , image (mathematics) , enhanced data rates for gsm evolution , feature detection (computer vision) , pattern recognition (psychology) , feature (linguistics) , linguistics , philosophy , operating system
Computer-aided analysis for cell images acquired by an electron microscope involves a range of image processing steps including edge detection and thresholding. The major problem encountered in automatic cell analysis is the possible presence of incomplete boundaries of cell features, which prevent the generation of cell feature details including all measurements as the boundaries include very tiny gaps. This paper presents a novel edge-linking technique based on an artificial neural process, which uses directional sensitivity derivatives from an edged image. The input signals applied to the neural layer are integrated with direction-sensitive information produced by an auxiliary algorithm, which interrogates all the pixels in the 2-D image in order to designate the specified direction in which each edge-end pixel should propagate. The proposed edge-linking technique, implemented as an image-processing algorithm for direction-sensitive selectiveness, provides an effective solution to the problem of porous boundaries encountered in biological cell image analysis.