Image segmentation using multiresolution wavelet analysis and expectation-maximization (EM) algorithm for digital mammography

Chen, Chi Hau; Lee, Gwo Giun

doi:10.1002/(sici)1098-1098(1997)8:5<491::aid-ima11>3.0.co;2-z

Cited by 29 publications

(4 citation statements)

References 15 publications

(17 reference statements)

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“…Therefore T−1 = TT and it is possible [4] to recover x from y by relation (2) In 2-dimensions x and y become 2 × 2 matrices. at first transform the columns of x, by pre-multiplying by T, and then the rows of the result by post-multiplying [4] by TT to find y = TxTT and in the next step x = TT yT (…”

Section: A Haar Wavelets In Image Decompositionmentioning

confidence: 99%

Leaf Image Segmentation Based On the Combination of Wavelet Transform and K Means Clustering

Valliammal¹

2012

IJARAI

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Abstract-This paper focuses on Discrete Wavelet Transform (DWT) associated with the K means clustering for efficient plant leaf image segmentation. Segmentation is a basic pre-processing task in many image processing applications and essential to separate plant leafs from the background. Locating and segmenting plants from the background in an automated way is a common challenge in the analysis of plant images. Image segmentation is typically used to locate objects and boundaries (lines, curves, etc.) in images. Image segmentation is a fundamental task in agriculture computer graphics vision. Although many methods are proposed, it is still difficult to accurately segment an arbitrary image by one particular method. In recent years, more and more attention has been paid to combine segmentation algorithms and information from multiple feature spaces (e.g. color, texture, and pattern) in order to improve segmentation results .The performance of the segmentation is analyzed by Jaccard, dice, variation of index and global consistency error method. The proposed approach is verified with real time plant leaf data base. The results of proposed approach gives better convergence when compare to existing segmentation method.

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Section: A Haar Wavelets In Image Decompositionmentioning

confidence: 99%

Leaf Image Segmentation Based On the Combination of Wavelet Transform and K Means Clustering

Valliammal¹

2012

IJARAI

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“…(1) Foreach rule R in ARC (the sorted set of rules) do { (2) if R matches I then R.count++ and keep R; (3) if R.count==1 then first.conf=R.conf; (4) else if (R.conf>first.conf-conf.t) R.count++ and keep R; (5) else exit; (6) } (7)…”

Section: Algorithm Classification Of a New Image (I)mentioning

confidence: 99%

“…All these things contribute to the decisions that are made on such images even more difficult. Different methods have been used to classify and detect anomalies in medical images, such as wavelets [4,15], fractal theory [8], statistical methods [6] and most of them used features extracted using image processing techniques [13]. In addition, some other methods were presented in the literature based on fuzzy set theory [3], Markov models [7] and neural networks [5,9].…”

Section: Introductionmentioning

confidence: 99%

Associative Classifiers for Medical Images

Antonie

Zaı̈ane

Coman

2003

Mining Multimedia and Complex Data

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Abstract. This paper presents two classification systems for medical images based on association rule mining. The system we propose consists of: a pre-processing phase, a phase for mining the resulted transactional database, and a final phase to organize the resulted association rules in a classification model. The experimental results show that the method performs well, reaching over 80% in accuracy. Moreover, this paper illustrates how important the data cleaning phase is in building an accurate data mining architecture for image classification.

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“…Segl and Kaufmann combined supervised shape classification and unsupervised image segmentation in an iterative procedure, deriving a method that can detect small objects in high spatial resolution panchromatic images and allowing target-oriented search for specific object shapes [ 13 ]. Chen and Lee introduced the expectation maximization algorithm to modify multi-resolution wavelet analysis model, proposing a segmentation algorithm for image classification that effectively achieves the texture segmentation of experimental images [ 14 ]. To quantitatively determine the optimal segmentation scale, Wang, Dong, and Chen proposed an image segmentation method combining superpixels and minimum spanning trees to seek the best image segmentation results.…”

Section: Introductionmentioning

confidence: 99%

Land use classification of open-pit mine based on multi-scale segmentation and random forest model

Zhang²,

Zhang

2022

PLoS ONE

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The mining industry production is an important pillar industry in China, while its extensive production activities have led to several ecological and environmental problems. Earth observation technology using high-resolution satellite imagery can help us efficiently obtain information on surface elements, surveying and monitoring various land occupation issues arising from open-pit mining production activities. Conventional pixel-based interpretation methods for high-resolution remote sensing images are restricted by “salt and pepper” noise caused by environmental factors, making it difficult to meet increasing requirements for monitoring accuracy. With the Jingxiang phosphorus mining area in Jingmen Hubei Province as the studied area, this paper uses a multi-scale segmentation algorithm to extract large-scale main characteristic information using a layered mask method based on the hierarchical structure of the image object. The remaining characteristic elements were classified and extracted in combination with the random forest model and characteristic factors to obtain land occupation information related mining industry production, which was compared with the results of the Classification and Regression Tree model. 23 characteristic factors in three aspects were selected, including spectral, geometric and texture characteristics. The methods employed in this study achieved 86% and 0.78 respectively in overall extraction accuracy analysis and the Kappa coefficient analysis, compared to 79% and 0.68 using the conventional method.

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Image segmentation using multiresolution wavelet analysis and expectation-maximization (EM) algorithm for digital mammography

Cited by 29 publications

References 15 publications

Leaf Image Segmentation Based On the Combination of Wavelet Transform and K Means Clustering

Leaf Image Segmentation Based On the Combination of Wavelet Transform and K Means Clustering

Associative Classifiers for Medical Images

Land use classification of open-pit mine based on multi-scale segmentation and random forest model

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