The segmentation of colorectal MRI images

Joshi, Niranjan; Bond, Sarah; Brady, Michael

doi:10.1016/j.media.2010.03.002

Cited by 16 publications

(14 citation statements)

References 24 publications

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“…The literature on monogenic representation almost exclusively relies on analysis applications: stereo analysis [30], motion estimation [35], segmentation [2], [27], texture classification [52]. A particular case is image demodulation [48], that produces a 'reconstructed' image.…”

Section: The Synthesis Issuementioning

confidence: 99%

“…α 3 is the angle between the y and z coordinates of the rotated perigee R T α2 R T α1 a − . The back conversion from (A, λ, ϕ, α 1 , α 2 α 3 ) first consists in getting back r + and r − : (29) and rotating them to retrieve a + and a − from equations (26)- (27). The original separate Fourier coefficients are equal to:…”

Section: B the Elliptical Modelmentioning

confidence: 99%

“…The signal is encoded with more sparsity than with standard wavelets, which globally improves most waveletbased applications. The seemingly most appropriate 2D extension for image analysis is the monogenic representation [15], of which a few wavelet counterparts have been proposed [23], [38], [48] and applied [2], [27], [30], [35], [52].…”

Section: Introductionmentioning

confidence: 99%

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Elliptical Monogenic Wavelets for the Analysis and Processing of Color Images

Soulard

Carré

2016

IEEE Trans. Signal Process.

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This paper studies and gives new algorithms for image processing based on monogenic wavelets. Existing greyscale monogenic filterbanks are reviewed and we reveal a lack of discussion about the synthesis part. The monogenic synthesis is therefore defined from the idea of wavelet modulation, and an innovative filterbank is constructed by using the Radon transform. The color extension is then investigated. First, the elliptical Fourier atom model is proposed to generalize the analytic signal representation for vector-valued signals. Then a color Riesz-transform is defined so as to construct color elliptical monogenic wavelets. Our Radon-based monogenic filterbank can be easily extended to color according to this definition. The proposed wavelet representation provides efficient analysis of local features in terms of shape and color, thanks to the concepts of amplitude, phase, orientation, and ellipse parameters. The synthesis from local features is deeply studied. We conclude the article by defining the color local frequency, proposing an estimation algorithm.

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Section: The Synthesis Issuementioning

confidence: 99%

Section: B the Elliptical Modelmentioning

confidence: 99%

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Elliptical Monogenic Wavelets for the Analysis and Processing of Color Images

Soulard

Carré

2016

IEEE Trans. Signal Process.

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“…An example is total mesorectal excision, for which the key parameter is the circumferential resection margin. Joshi, Bond, and Brady [2010] reported a system based on level segmentation, constrained by detection of parts of the mesorectum boundary using the monogenic signal.…”

Section: Accepted Manuscriptmentioning

confidence: 99%

Oncological image analysis

Brady

Highnam²,

Irving

et al. 2016

Medical Image Analysis

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Highlights Medical image analysis applied to cancer has to date only addressed a very small subset of the "hallmarks of cancer".  A number of opportunities and challenges for research over the next decade in oncological image analysis are outlined. AbstractCancer is one of the world's major healthcare challenges and, as such, an important application of medical image analysis. After a brief introduction to cancer, we summarise some of the the major developments in oncological image analysis over the past 20 years, but concentrating those in the authors' laboratories, and then outline opportunities and challenges for the next decade.

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“…We assumed that the location of a lymph node is known in a given image volume. We have previously published semi-automatic computeraided procedures [17,18] to detect locations of possible lymph node candidates in a given MRI image volume. The first step of the computer algorithm is image segmentation.…”

Section: Description Of the Computer Algorithmmentioning

confidence: 99%

A computer-aided algorithm to quantitatively predict lymph node status on MRI in rectal cancer

Tse¹,

Joshi²,

Anderson³

et al. 2012

BJR

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Objective: The aim of this study was to demonstrate the principle of supporting radiologists by using a computer algorithm to quantitatively analyse MRI morphological features used by radiologists to predict the presence or absence of metastatic disease in local lymph nodes in rectal cancer. Methods: A computer algorithm was developed to extract and quantify the following morphological features from MR images: chemical shift artefact; relative mean signal intensity; signal heterogeneity; and nodal size (volume or maximum diameter). Computed predictions on nodal involvement were generated using quantified features in isolation or in combinations. Accuracies of the predictions were assessed against a set of 43 lymph nodes, determined by radiologists as benign (20 nodes) or malignant (23 nodes). Results: Predictions using combinations of quantified features were more accurate than predictions using individual features (0.67-0.86 vs 0.58-0.77, respectively). The algorithm was more accurate when three-dimensional images were used (0.58-0.86) than when only middle image slices (two-dimensional) were used (0.47-0.72). Maximum node diameter was more accurate than node volume in representing the nodal size feature; combinations including maximum node diameter gave accuracies up to 0.91. Conclusion: We have developed a computer algorithm that can support radiologists by quantitatively analysing morphological features of lymph nodes on MRI in the context of rectal cancer nodal staging. We have shown that this algorithm can combine these quantitative indices to generate computed predictions of nodal status which closely match radiological assessment. This study provides support for the feasibility of computer-assisted reading in nodal staging, but requires further refinement and validation with larger data sets. Rectal cancer is a common and major cause of mortality in the Western world. The treatment of rectal cancer has seen significant progress over recent years with the development of new adjuvant and neoadjuvant therapies [1,2], and new surgical techniques such as transanal endoscopic microsurgery (TEM). These advances have made the accurate nodal staging of rectal cancer increasingly important in guiding the choice of treatment options for each individual patient. Confident exclusion of nodal involvement could enable patients with earlystage (T1) rectal cancer to undergo local excision such as TEM, with its advantage of reduced peri and postoperative morbidity compared with conventional surgical techniques [3][4][5]. Moreover, the accurate identification of nodes involved by tumour may identify patients who would benefit from either neoadjuvant chemoradiotherapy prior to surgery or adjuvant chemotherapy post-neoadjuvant chemoradiotherapy and resection where pre-operative treatment prevents accurate identification of pre-treatment disease status. Currently, rectal cancer staging is performed with MRI, which has established itself as the reference standard [6][7][8].Different techniques have been evaluated to address th...

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The segmentation of colorectal MRI images

Cited by 16 publications

References 24 publications

Elliptical Monogenic Wavelets for the Analysis and Processing of Color Images

Elliptical Monogenic Wavelets for the Analysis and Processing of Color Images

Oncological image analysis

A computer-aided algorithm to quantitatively predict lymph node status on MRI in rectal cancer

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