Combining wavelets and watersheds for robust multiscale image segmentation

Jung, Cláudio Rosito

doi:10.1016/j.imavis.2006.01.002

Cited by 42 publications

(18 citation statements)

References 30 publications

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“…The two-dimensional discrete wavelet transform can decompose an image into 4 different resolutions of sub-bands [11]. Each one of these sub-bands are produced with the half size of the original image, corresponding to high frequencies in the horizontal direction and low frequencies in the vertical direction HL (horizontal details), low frequencies in the horizontal direction and high frequencies in the vertical direction LH (vertical details), high frequencies in both directions HH (diagonal details) and low frequencies in both directions LL (approximation coefficients) [12,13].…”

Section: Two Dimensional Discrete Wavelet Transform (Dwt2)mentioning

confidence: 99%

Automatic DWT2 thresholding based segmentation of the pigmented skin lesions in dermatoscopic images

Razazzadeh

Khalili

2014

IJET

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The segmentation is the most important step to automatic diagnosis of the skin lesions. In this paper, a DWT2 thresholding based segmentation of dermatoscopic images has been proposed to diagnose of the pigmented skin lesions. In the proposed method, first, the image is converted to YUV channels and after denoising and contrast enhancement of the second channel of the converted image, it is decomposed to wavelet transform in two levels. Then, to more specificity and accuracy of segmentation, the Otsu's thresholding method is applied on each sub-band of the second level of decomposed image and four thresholds are achieved. Subsequently, using adding all thresholds a new threshold is obtained and applied on the second level reconstructed image to achieve a binary image. Finally, post-processing is applied on this binary image using algorithms of morphological reconstructions, to increase the sensitivity. The experimental results show that the proposed method increases the accuracy to 90.97%, and specificity to 99.76%, compared with the other existing methods.

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Section: Two Dimensional Discrete Wavelet Transform (Dwt2)mentioning

confidence: 99%

Automatic DWT2 thresholding based segmentation of the pigmented skin lesions in dermatoscopic images

Razazzadeh

Khalili

2014

IJET

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“…Mostafa et al proposed image segmentation using wavelet based multi resolution Expectation Maximum (EM) algorithm [8]. But the main drawback of this algorithm is that it is based on identical and independent distribution of pixel intensities which may not be the case with noisy images.…”

Section: Related Workmentioning

confidence: 99%

Application of Wavelet based K-means Algorithm in Mammogram Segmentation

Dalmiya¹,

Dasgupta²,

Datta³

2012

IJCA

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Research in image processing has gained lots of momentum during past two decades. Now-a-days image processing techniques have found their way into computer vision, image compression, image security, medical imaging and more. This paper presents a research on mammography images using wavelet transformation and K -means clustering for cancer tumor mass segmentation. The first step is to perform image segmentation. It allows distinguishing masses and micro calcifications from background tissue. In this paper wavelet transformation and K-means clustering algorithm have been used for intensity based segmentation. The proposed algorithm is robust against noise. In this case, discrete wavelet transform (DWT) is used to extract high level details from MRI images. The processed image is added to the original image to get the sharpened image. Then K-means algorithm is applied to the sharpened image in which the tumor region can be located using the thresholding method. This paper validates the algorithm by detecting tumor region from an MRI image of mammogram. The combination of noise-robust nature of applied processes and the simple K-means algorithm gives better results. General TermsImage Processing

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“…is will reduce the accuracy and efficiency of multiscale information extraction from high spatial resolution images [13][14][15]. Many methods have been used to select optimal parameters for multiscale segmentation [16][17][18][19][20][21][22][23][24][25]; however, optimal segmentation parameters for an overall image may not suitable for different objects when processing large heterogeneous images [26,27]. A key issue that remains to be resolved is to determine a suitable segmentation scale that allows different objects and phenomena to be characterized in a single image [28,29].…”

Section: Introductionmentioning

confidence: 99%

Stratified Object-Oriented Image Classification Based on Remote Sensing Image Scene Division

Zhou

Ming

et al. 2018

Journal of Spectroscopy

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e traditional remote sensing image segmentation method uses the same set of parameters for the entire image. However, due to objects' scale-dependent nature, the optimal segmentation parameters for an overall image may not be suitable for all objects. According to the idea of spatial dependence, the same kind of objects, which have the similar spatial scale, often gather in the same scene and form a scene. Based on this scenario, this paper proposes a stratified object-oriented image analysis method based on remote sensing image scene division. is method firstly uses middle semantic which can reflect an image's visual complexity to classify the remote sensing image into different scenes, and then within each scene, an improved grid search algorithm is employed to optimize the segmentation result of each scene, so that the optimal scale can be utmostly adopted for each scene. Because the complexity of data is effectively reduced by stratified processing, local scale optimization ensures the overall classification accuracy of the whole image, which is practically meaningful for remote sensing geo-application.

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Combining wavelets and watersheds for robust multiscale image segmentation

Cited by 42 publications

References 30 publications

Automatic DWT2 thresholding based segmentation of the pigmented skin lesions in dermatoscopic images

Automatic DWT2 thresholding based segmentation of the pigmented skin lesions in dermatoscopic images

Application of Wavelet based K-means Algorithm in Mammogram Segmentation

Stratified Object-Oriented Image Classification Based on Remote Sensing Image Scene Division

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