Statistical and Adaptive Approaches for Optimal Segmentation in Medical Images

Yang, Shuyu; Mitra, Sunanda

doi:10.1007/0-306-48606-7_6

Cited by 4 publications

(3 citation statements)

References 56 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As can be expected, results deteriorate if images are especially poor. A combination of more sophisticated clustering techniques [1,14] and active contour will be used in future to improve the segmentation. …”

Section: Eq (3)mentioning

confidence: 99%

A multispectral digital Cervigram analyzer in the wavelet domain for early detection of cervical cancer

Yang¹,

Guo²,

King³

et al. 2004

SPIE Proceedings

Self Cite

View full text Add to dashboard Cite

The significance and need for expert interpretation of cervigrams TM (images of the cervix) in the study of the uterine cervix changes and pre-neoplasic lesions preceding cervical cancer are being investigated. The National Cancer Institute has collected a unique dataset taken from patients with normal cervixes and at various stages of cervical pre-cancer and cancer. This dataset allows us the opportunity for studying the uterine cervix changes for validating the potential of automated classification and recognition algorithms in discriminating cervical neoplasia and normal tissue. Pilot studies have been designed (1) to evaluate the effect of image transformation and optimal color mapping on the accepted levels of compression needed for effective dissemination of cervical image data over a network and (2) for automated detection of lesions from feature extraction, registration, and segmentation of lesions in cervix image sequences. In this paper, we present the results of the effectiveness of a novel, wavelet based, multi-spectral analyzer in retaining diagnostic features in encoded cervical images, thus allowing investigation on the potential of automated detection of lesions in cervix image sequences using automated registration, color transformation and bit-rate control, and a statistical segmentation approach.

show abstract

Section: Eq (3)mentioning

confidence: 99%

A multispectral digital Cervigram analyzer in the wavelet domain for early detection of cervical cancer

Yang¹,

Guo²,

King³

et al. 2004

SPIE Proceedings

Self Cite

View full text Add to dashboard Cite

show abstract

“…This phase plays a crucial role [ 12 ]: any non segmented lesion at this stage will be irremediably lost for any further analysis. While a wide variety of segmentation approaches have been proposed, there is no standard algorithm that can ensure high levels of accuracy for all imaging applications [ 13 - 15 ]. Furthermore, many segmentation methods rely on specific testing on an actual database [ 16 ] and the performance depends on database specificities.…”

Section: Introductionmentioning

confidence: 99%

“…Within this approach, the process of feature clustering becomes the crucial part of the segmentation algorithm. The main advantage of this approach is that the method does not require the use of a training set [ 15 ]. Towards the end of the last century, a new promising nonparametric method of clustering relying on the physical properties of the inhomogeneous Potts model has been proposed by Blatt, Wiseman and Domany [ 22 ]; a similar approach was proposed in terms of coupled chaotic dynamical networks by Manrubia and Mikhalkov [ 23 ] and has been further refined and restated with coupled chaotic maps by L. Angelini et al [ 24 ].…”

Section: Introductionmentioning

confidence: 99%

Mammographic images segmentation based on chaotic map clustering algorithm

et al. 2014

View full text Add to dashboard Cite

BackgroundThis work investigates the applicability of a novel clustering approach to the segmentation of mammographic digital images. The chaotic map clustering algorithm is used to group together similar subsets of image pixels resulting in a medically meaningful partition of the mammography.MethodsThe image is divided into pixels subsets characterized by a set of conveniently chosen features and each of the corresponding points in the feature space is associated to a map. A mutual coupling strength between the maps depending on the associated distance between feature space points is subsequently introduced. On the system of maps, the simulated evolution through chaotic dynamics leads to its natural partitioning, which corresponds to a particular segmentation scheme of the initial mammographic image.ResultsThe system provides a high recognition rate for small mass lesions (about 94% correctly segmented inside the breast) and the reproduction of the shape of regions with denser micro-calcifications in about 2/3 of the cases, while being less effective on identification of larger mass lesions.ConclusionsWe can summarize our analysis by asserting that due to the particularities of the mammographic images, the chaotic map clustering algorithm should not be used as the sole method of segmentation. It is rather the joint use of this method along with other segmentation techniques that could be successfully used for increasing the segmentation performance and for providing extra information for the subsequent analysis stages such as the classification of the segmented ROI.

show abstract

Biomedical Image Segmentation Based on Morphological Spectra

Kulikowski

Przytulska

2009

IFMBE Proceedings

View full text Add to dashboard Cite

Statistical and Adaptive Approaches for Optimal Segmentation in Medical Images

Cited by 4 publications

References 56 publications

A multispectral digital Cervigram analyzer in the wavelet domain for early detection of cervical cancer

A multispectral digital Cervigram analyzer in the wavelet domain for early detection of cervical cancer

Mammographic images segmentation based on chaotic map clustering algorithm

Biomedical Image Segmentation Based on Morphological Spectra

Contact Info

Product

Resources

About