An adaptive local thresholding method for microscope based high content analysis (HCA) is proposed. Cell micrographs of HCA contain detailed objects both in and out of focus, which cannot be correctly segmented by global thresholding methods. The proposed method utilizes adaptive local neighborhood size and double thresholding, and is able to produce segmentations that conform closely to percep...

BACKGROUND AND PURPOSE The accuracy of automatic tissue segmentation methods can be affected by the presence of hypointense white matter lesions during the tissue segmentation process. Our aim was to evaluate the impact of MS white matter lesions on the brain tissue measurements of 6 well-known segmentation techniques. These include straightforward techniques such as Artificial Neural Network a...

In this paper, we present a fully automated image segmentation method based on an algorithm that provides adaptive plasticity in function approximation problems: the deformable (feature) map (DM) algorithm. The DM approach reduces a class of similar function approximation problems to the explicit supervised one-shot training of a single data set. This is followed by a subsequent, appropriate si...

This paper proposes an unsupervised segmentation scheme for cell nuclei. This method computes the cell nuclei by using adaptive active contour modelling which is driven by the morphology method. Firstly, morphology is used to enhance the gray level values of cell nuclei. Then binary cell nuclei is acquired by using an image subtraction technique. Secondly, the masks of cell nuclei are utilized ...

Moving object segmentation has its own niche as an important topic in computer vision. It has avidly being pursued by researchers. Background subtraction method is generally used for segmenting moving objects. This method may also classify shadows as part of detected moving objects. Therefore, shadow detection and removal is an important step employed after moving object segmentation. However, ...

Clustering algorithms have successfully been applied as a digital image segmentation technique in various fields and applications. However, those clustering algorithms are only applicable for specific images such as medical images, microscopic images etc. In this paper, we present a new clustering algorithm called Adaptive FuzzyK-means (AFKM) clustering for image segmentation which could be app...

A new spectral-spatial classification scheme for hyperspectral images is presented. Pixel-wise Support Vector Machines classification and segmentation are performed independently, and then the results are combined, using the majority vote approach. Thus, every region from a segmentation map defines an adaptive neighborhood for all the pixels within this region. The use of several segmentation t...

To address multiple motions and deformable objects’ motions encountered in existing region-based approaches, an automatic video object (VO) segmentation methodology is proposed in this paper by exploiting the duality of image segmentation and motion estimation such that spatial and temporal information could assist each other to jointly yieldmuch improved segmentation results. The key novelties...

We use Bayesian methods to design cellular neural networks for signal processing tasks and the Boltzmann Machine learning rule for parameter estimation. The learning rule can be used for models with uhidden" units, or for compietely unsupervised learning. The latter is exemplified by unsupervised adaptation of an image segmentation cellular network, in particular we apply the learning rule to a...

An autoadaptive neuro-fuzzy segmentation and edge detection architecture is presented. The system consist of a multilayer perceptron (MLP)-like network that performs image segmentation by adaptive thresholding of the input image using labels automatically pre-selected by a fuzzy clustering technique. The proposed architecture is feedforward, but unlike the conventional MLP the learning is unsup...