Pipeline architectures for recursive morphological operations

Recent advances in morphological cell image analysis

This paper summarizes the recent advances in image processing methods for morphological cell analysis. The topic of morphological analysis has received much attention with the increasing demands in both bioinformatics and biomedical applications. Among many factors that affect the diagnosis of a disease, morphological cell analysis and statistics have made great contributions to results and effects for a doctor. Morphological cell analysis finds the cellar shape, cellar regularity, classification, statistics, diagnosis, and so forth. In the last 20 years, about 1000 publications have reported the use of morphological cell analysis in biomedical research. Relevant solutions encompass a rather wide application area, such as cell clumps segmentation, morphological characteristics extraction, 3D reconstruction, abnormal cells identification, and statistical analysis. These reports are summarized in this paper to enable easy referral to suitable methods for practical solutions. Representative contributions and future research trends are also addressed.

The efficient algorithms for achieving Euclidean distance transformation

Euclidean distance transformation (EDT) is used to convert a digital binary image consisting of object (foreground) and nonobject (background) pixels into another image where each pixel has a value of the minimum Euclidean distance from nonobject pixels. In this paper, the improved iterative erosion algorithm is proposed to avoid the redundant calculations in the iterative erosion algorithm. Furthermore, to avoid the iterative operations, the two-scan-based algorithm by a deriving approach is developed for achieving EDT correctly and efficiently in a constant time. Besides, we discover when obstacles appear in the image, many algorithms cannot achieve the correct EDT except our two-scan-based algorithm. Moreover, the two-scan-based algorithm does not require the additional cost of preprocessing or relative-coordinates recording.

AN INTELLIGENT HYBRID APPROACH FOR CONTENT-BASED IMAGE RETRIEVAL

The paper presents an intelligent hybrid approach for content-based image retrieval based on texture feature. The proposed approach employs an Auto–Associative Neural Network (AANN) for feature extraction and a Multi–Layer Perceptron (MLP) with a single hidden layer for the classification. Two intelligent approaches such as AANN–MLP and statistical–MLP were investigated. The performance of the proposed approaches was evaluated on a large benchmark database of texture patterns. The results are very promising compared to other existing traditional and intelligent techniques. Some of the experimental results conducted during the investigation, comparative analysis of the results and suggestions to select the appropriate techniques for texture feature extraction and classification are presented in this paper.

SCENOGRAM — SCENE ANALYSIS USING COMPOSITENEURAL OSCILLATORY-BASED ELASTIC GRAPH MODEL

Scene analysis is so far one of the most important topics in machine vision. In this paper, we present an integrated scene analysis model, namely SCENOGRAM (Scene analysis using CompositENeural Oscillatory-based elastic GRAph Model). Basically the proposed scene analyzer is based on the integration of the composite neural oscillatory model with our elastic graph dynamic link model. The system involves: (1) multifrequency bands feature extraction scheme using Gabor filters, (2) automatic figure-ground object segmentation using a composite neural oscillatory model, and (3) object matching using an elastic graph dynamic link model.

From the implementation point of view, we introduce an intelligent agent based scene analysis and object identification solution using the SCENOGRAM technology. From the experimental point of view, a scene gallery of over 6000 color scene images is used for automatic scene segmentation testing and object identification test. An overall correct invariant facial recognition rate of over 87% is attained. It is anticipated that the implementation of the SCENOGRAM can provide an invariant and higher-order intelligent object (pattern) encoding, searching and identification solution for future intelligent e-Business.