Multiscale image segmentation using a hierarchical self-organizing map

Bhandarkar, Suchendra M.; Koh, Jeongwan; Suk, Minsoo

doi:10.1016/s0925-2312(96)00048-3

Cited by 69 publications

(35 citation statements)

References 12 publications

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“…Bhandarkar et.al. [2] . Learning process consists of sequential corrections of the vectors representing neurons.…”

Section: Implementation Of Hsom and Fcm Algorithmmentioning

confidence: 99%

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An Improved Implementation of Brain Tumor Detection Using Segmentation Based on Neuro Fuzzy Technique

Murugavalli¹,

Rajamani²

2007

J. of Computer Science

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Implementation of a neuro-fuzzy segmentation process of the MRI data is presented in this study to detect various tissues like white matter, gray matter, csf and tumor. The advantage of hierarchical self organizing map and fuzzy c means algorithms are used to classify the image layer by layer. The lowest level weight vector is achieved by the abstraction level. We have also achieved a higher value of tumor pixels by this neuro-fuzzy approach. The computation speed of the proposed method is also studied. The multilayer segmentation results of the neuro fuzzy are shown to have interesting consequences from the viewpoint of clinical diagnosis. Neuro fuzzy technique shows that MRI brain tumor segmentation using HSOM-FCM also perform more accurate one.

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“…Bhandarkar et.al. [2] . Learning process consists of sequential corrections of the vectors representing neurons.…”

Section: Implementation Of Hsom and Fcm Algorithmmentioning

confidence: 99%

“…Magnetic Resonance Imaging (MRI) is the state-ofthe-art medical imaging technology which allows cross sectional view of the body with unprecedented tissue contrast [1][2] . MRI plays an important role in assessing pathological conditions of the ankle, foot and brain.…”

Section: Introductionmentioning

confidence: 99%

An Improved Implementation of Brain Tumor Detection Using Segmentation Based on Neuro Fuzzy Technique

Murugavalli¹,

Rajamani²

2007

J. of Computer Science

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“…SOM are very often used in problems of the analysis of large data structures e.g. in the problems of clustering or classification [9], [10], [11], [12], image processing [13], [14], [15], robotics [16], [17], time series forecasting [18], [19], [20] and faults detection and identification [21], [22], [23].…”

Section: Introductionmentioning

confidence: 99%

Faults diagnosis using self-organizing maps: A case study on the DAMADICS benchmark problem

Katunin

Amarowicz

Chrzanowski

2015

Annals of Computer Science and Information Systems

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Abstract-This paper deals with a method of faults detection and identification based on the clusterization of the multiple diagnostic signals. Various types of faults and character of their occurrence were simulated using DAMADICS Benchmark Process Control System. A great advantage of the applied approach based on self-organizing (Kohonen) maps is that even the smallest differences in signals allow for detection, isolation and identification of type of occurred faults with respect to the healthy condition of the investigated system based on the unsupervised learning. It was shown that in some cases the faults, which are undetectable during monitoring of simple heuristic and statistical parameters and other previously applied methods, are recognizable when the approach based on self-organizing maps is applied. The case studies presented in this paper show the faults detection procedure as well as clusterization of types and successful classification of almost all the unique faulty states of the investigated system.

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“…Its techniques are being applied with remarkable success to several cases of computer vision and perception, but most of these problems are fairly simple in nature and still cannot handle real-time requirements. 2,7,21 The difficulty with scaling up to complex tasks is that inductive learning methods require a very large number of training patterns in order to generalize correctly from high-density sensor information (like video cameras). 16,20,22 Deformable models have been intensively studied in image analysis through the last decade, 9,18 and are used for detection and recognition of flexible or rigid models under various viewing conditions.…”

Section: Introductionmentioning

confidence: 99%

Object learning and detection using evolutionary deformable models for mobile robot navigation

Mata

Armingol²,

Andrés

et al. 2008

Robotica

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SUMMARYDeformable models have been studied in image analysis over the last decade and used for recognition of flexible or rigid templates under diverse viewing conditions. This article addresses the question of how to define a deformable model for a real-time color vision system for mobile robot navigation. Instead of receiving the detailed model definition from the user, the algorithm extracts and learns the information from each object automatically. How well a model represents the template that exists in the image is measured by an energy function. Its minimum corresponds to the model that best fits with the image and it is found by a genetic algorithm that handles the model deformation. At a later stage, if there is symbolic information inside the object, it is extracted and interpreted using a neural network. The resulting perception module has been integrated successfully in a complex navigation system. Various experimental results in real environments are presented in this article, showing the effectiveness and capacity of the system.

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Multiscale image segmentation using a hierarchical self-organizing map

Cited by 69 publications

References 12 publications

An Improved Implementation of Brain Tumor Detection Using Segmentation Based on Neuro Fuzzy Technique

An Improved Implementation of Brain Tumor Detection Using Segmentation Based on Neuro Fuzzy Technique

Faults diagnosis using self-organizing maps: A case study on the DAMADICS benchmark problem

Object learning and detection using evolutionary deformable models for mobile robot navigation

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