Morphological Methods for Biomedical Image Analysis

Goutsias, John; Batman, Sinan

doi:10.1117/3.831079.ch4

Cited by 39 publications

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“…In this paper, we propose a scheme for detecting and tracking targets in FLIR image sequences based on a class of morphological operators known as connected operators (see Vincent, 1993;Salembier and Serra, 1995;Salembier et al, 1998;Goutsias and Batman, 2000). Our approach results in an effective and robust technique for clutter suppression and normalization, and produces consistent detection performance over a wide range of illumination conditions.…”

Section: Introductionmentioning

confidence: 99%

Automatic target detection and tracking in forward-looking infrared image sequences using morphological connected operators

Braga-Neto

Choudhary

Goutsias

2004

J. Electron. Imaging

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We propose a method for automatic target detection and tracking in forward-looking infrared (FLIR) image sequences. We use morphological connected operators to extract and track targets of interest and remove undesirable clutter. The design of these operators is based on general size, connectivity and motion criteria, using spatial intraframe and temporal interframe information. In a first step, an image sequence is filtered on a frame-by-frame basis to remove background and residual clutter and to enhance the presence of targets. Detections extracted from the first step are passed to a second step for motion-based analysis. This step exploits the spatiotemporal correlation of the data, stated in terms of a connectivity criterion along the time dimension. The proposed method is suitable for piplined implementation or time progressive coding/transmission, since only a few frames are considered at a time. Experimental results, obtained with real FLIR image sequences, illustrating a wide variety of target and clutter variability, demonstrate the effectiveness and robustness of the proposed method.

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Section: Introductionmentioning

confidence: 99%

Automatic target detection and tracking in forward-looking infrared image sequences using morphological connected operators

Braga-Neto

Choudhary

Goutsias

2004

J. Electron. Imaging

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“…where, F s (t) is a cross-section of the image intensity f and F s (t)|s = 1, 2, 3 are grains of the cross-section F (t) of the image f and a is the threshold level [12]. On the other hand area closing is used to fill in the holes in the image, whose area is smaller than a given value.…”

Section: Morphological Methods For Spot Segmentationmentioning

confidence: 99%

“…Every regional minimum M in reg has a catchment basin C(M in reg ) associated with it, which is a collection of all points of the topographic surface of f , such that a drop of water falling at any point slides along the surface until it reaches M in reg [10], [13]. Now, by flooding the topographic surface of an image from its regional minimum and preventing the merging of water coming from different sources, we partition the image into two different sets; the catchment basins and the watershed lines, where each catchment basin contains one and only one regional minimum [12]. Using the above analogy we can define the watershed transform as,…”

Section: Morphological Methods For Spot Segmentationmentioning

confidence: 99%

“…5. Now we apply the watershed transform (12) to the negation of the original image using the markers found previously and using the box-structuring element to define connectivity. These watershed lines are used to act as external markers, which mark the crest lines of the original image Fig.…”

Section: Spot Segmentation Of Gene Microarray Imagementioning

confidence: 99%

“…Two iterations of the alternating sequential filter (ASF), characterized by a cross structuring element [12] having unit radius, are applied to the image in Fig. 4 while using the sequence of opening followed by closing operators (8),(9),(10), (11).…”

Section: Spot Segmentation Of Gene Microarray Imagementioning

confidence: 99%

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Mathematical morphology applied to spot segmentation and quantification of gene microarray images

Siddiqui

Hero

Siddiqui³

Conference Record of the Thirty-Sixth Asilomar Conference on Signals, Systems and Computers, 2002.

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Abstract-DNA microarray technology is a very powerful technique used in modern biology which is extensively used for identification of sequence (gene/gene mutation) and determination of gene expression. A typical gene microarray image consists of a few hundred to several thousand spots and the extent of hybridization of these spots determines the level of gene expression (abundance) in the sample. The massive scale and variability of gene microarray data creates new challenging problems of gene clustering, feature extraction and data mining. A major issue in gene microarray data analysis is to accurately quantify spot shapes and intensities of microarray image. In this paper we propose a method for performing accurate spot segmentation of a microarray image, using morphological image analysis techniques, followed by quantification of the shapes of the segmented spots using B-Splines.

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2013

Stochastic Geometry and Its Applications

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Morphological Methods for Biomedical Image Analysis

Cited by 39 publications

References 0 publications

Automatic target detection and tracking in forward-looking infrared image sequences using morphological connected operators

Automatic target detection and tracking in forward-looking infrared image sequences using morphological connected operators

Mathematical morphology applied to spot segmentation and quantification of gene microarray images

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