EigenBlock algorithm for change detection – An application of adaptive dictionary learning techniques

Nika, Varvara; Babyn, Paul; Zhu, Hongmei

doi:10.1016/j.jocs.2013.10.008

Cited by 4 publications

(7 citation statements)

References 12 publications

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“…The detailed explanation can be found in Nika et al 9 The EigenBlockCD extends previous work 5,7 on dictionary learning techniques in face recognition problems. Although those methods utilize a database of images to find the perfect match.…”

Section: Eigenblockcdsupporting

confidence: 53%

“…24. We compared our results with the results of several algorithms as presented by their authors, 1,2,8,10 which are shown in Table 1.…”

Section: Simulations With Serial Mr Imagesmentioning

confidence: 99%

“…8 However, because of the long processing time of l 1 minimization, we adapted the rational behind AEDL and established a simpler but effective new algorithm, namely, EigenBlock change detection (EigenBlockCD). 9 Both the AEDL aknd the EigenBlockCD methods extend previous work 5,7 on dictionary learning techniques in face recognition problems and SAR images. The AEDL and the EigenBlockCD algorithms use local dictionaries learned from one of the images to detect the changes in the consecutive MRI follow-up scans.…”

Section: Introductionmentioning

confidence: 99%

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Change detection of medical images using dictionary learning techniques and principal component analysis

2014

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Abstract. Automatic change detection methods for identifying the changes of serial MR images taken at different times are of great interest to radiologists. The majority of existing change detection methods in medical imaging, and those of brain images in particular, include many preprocessing steps and rely mostly on statistical analysis of magnetic resonance imaging (MRI) scans. Although most methods utilize registration software, tissue classification remains a difficult and overwhelming task. Recently, dictionary learning techniques are being used in many areas of image processing, such as image surveillance, face recognition, remote sensing, and medical imaging. We present an improved version of the EigenBlockCD algorithm, named the EigenBlockCD-2. The EigenBlockCD-2 algorithm performs an initial global registration and identifies the changes between serial MR images of the brain. Blocks of pixels from a baseline scan are used to train local dictionaries to detect changes in the follow-up scan. We use PCA to reduce the dimensionality of the local dictionaries and the redundancy of data. Choosing the appropriate distance measure significantly affects the performance of our algorithm. We examine the differences between L 1 and L 2 norms as two possible similarity measures in the improved EigenBlockCD-2 algorithm. We show the advantages of the L 2 norm over the L 1 norm both theoretically and numerically. We also demonstrate the performance of the new EigenBlockCD-2 algorithm for detecting changes of MR images and compare our results with those provided in the recent literature. Experimental results with both simulated and real MRI scans show that our improved EigenBlockCD-2 algorithm outperforms the previous methods. It detects clinical changes while ignoring the changes due to the patient's position and other acquisition artifacts.

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Section: Eigenblockcdsupporting

confidence: 53%

“…24. We compared our results with the results of several algorithms as presented by their authors, 1,2,8,10 which are shown in Table 1.…”

Section: Simulations With Serial Mr Imagesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Change detection of medical images using dictionary learning techniques and principal component analysis

2014

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“…In previous publications, we extended primary work in the area of background modeling and dictionary learning techniques and developed three variant methods [17][18][19][20]. Our first algorithm, (Adaptive EigenBlock Dictionary Learning (AEDL)) [17] , performs image registration locally to capture the local spatial changes in the test image using a series of local sparse minimization processes and the knowledge of compressive sensing. The method takes a pair of images as input, and gives two images as output: the recovered sparse image aligned with the second one and the image of detected changes.…”

Section: Introductionmentioning

confidence: 99%

“…The reconstructions of overlapping blocks using L 1 minimization algorithms make the AEDL algorithm computationally expensive. To reduce the running time of the algorithm, we designed the second algorithm, EigenBlock Change Detection (EigenBlockCD) [18] , which uses the L 2 norm as a similarity measure to learn the dictionary. The method takes two images as input and automatically produces an image which contains only clinically related changes.…”

Section: Introductionmentioning

confidence: 99%

Change Detection of Medical Images for Three Dimensional Volumetric Data

Nika¹,

Babyn²,

Zhu³

2015

J Theor Comput Sci

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Recently, background modeling and sparse representation methods have been used successfully in video surveillance [14,16]. In Ref [13] background subtracted images are recovered using compressive sensing (CS). Work of Ref [17] proposed the use of principle component pursuit method to detect foreground changes in video surveillance. The work is based on low-rank and sparse decomposition of image matrices. Robust principal component analysis has applications in

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Validation of White-Matter Lesion Change Detection Methods on a Novel Publicly Available MRI Image Database

et al. 2016

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Changes of white-matter lesions (WMLs) are good predictors of the progression of neurodegenerative diseases like multiple sclerosis (MS). Based on longitudinal magnetic resonance (MR) imaging the changes can be monitored, while the need for their accurate and reliable quantification led to the development of several automated MR image analysis methods. However, an objective comparison of the methods is difficult, because publicly unavailable validation datasets with ground truth and different sets of performance metrics were used. In this study, we acquired longitudinal MR datasets of 20 MS patients, in which brain regions were extracted, spatially aligned and intensity normalized. Two expert raters then delineated and jointly revised the WML changes on subtracted baseline and follow-up MR images to obtain ground truth WML segmentations. The main contribution of this paper is an objective, quantitative and systematic evaluation of two unsupervised and one supervised intensity based change detection method on the publicly available datasets with ground truth segmentations, using common pre- and post-processing steps and common evaluation metrics. Besides, different combinations of the two main steps of the studied change detection methods, i.e. dissimilarity map construction and its segmentation, were tested to identify the best performing combination.

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EigenBlock algorithm for change detection – An application of adaptive dictionary learning techniques

Cited by 4 publications

References 12 publications

Change detection of medical images using dictionary learning techniques and principal component analysis

Change detection of medical images using dictionary learning techniques and principal component analysis

Change Detection of Medical Images for Three Dimensional Volumetric Data

Validation of White-Matter Lesion Change Detection Methods on a Novel Publicly Available MRI Image Database

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