A model of contour extraction including multiple scales, flexible inhibition and attention

Cara, Giuseppe-Emiliano La; Ursino, Mauro

doi:10.1016/j.neunet.2007.11.003

Cited by 17 publications

(19 citation statements)

References 82 publications

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“…In fact, there has been a growing interest in the use of multi-scale analysis for image processing such as contour detection (Joshi and Sivaswamy, 2005;La Cara and Ursino, 2008;Liang et al, 1999;Lindeberg, 1998;Mallat, 1989;Papari et al, 2007;Ren, 2008), based on extensive experimental evidence supporting that multiscale mechanism is psychophysically plausible for image perception (Campbell and Robson, 1968;Julesz, 1962;Mermillod et al, 2005;Morrone and Burr, 1997;Oliva and Schyns, 1997). However, the role of interactions that exist between different scales in biological vision system is not well understood.…”

Section: Discussionmentioning

confidence: 99%

“…Psychophysically, the associated operations at the two coarse and fine scales in our model could be restrictedly interpreted as the subsequent pre-attentive and attentive stages in image perception (La Cara and Ursino, 2008;Li, 2002;Zhaoping and Dayan, 2006). Gabor filters with larger scales analyze the visual field at a coarser level of spatial frequency and try to preattend and identify where objects are.…”

Section: Other Related Workmentioning

confidence: 99%

“…Such hypothesis is thought to be reasonable based on the underlying physiological substrate of multi-resolution wavelet analysis (Campbell and Robson, 1968;Mermillod et al, 2005;Morrone and Burr, 1997;Oliva and Schyns, 1997;Papari et al, 2007;Mallat, 1989). In fact, multiresolution mechanism has been successfully used in contour detection (Joshi and Sivaswamy, 2005;La Cara and Ursino, 2008;Liang et al, 1999;Lindeberg, 1998;Mallat, 1989;Papari et al, 2007;Ren, 2008), and this issue is further discussed in the Summary and discussion section.…”

Section: Introductionmentioning

confidence: 96%

“…Based on the insight understanding of nCRF inhibition mechanism, several nCRF inhibition-based contour detection models have recently been proposed in the literature (e.g., Grigorescu et al, 2003;La Cara and Ursino, 2008;Li, 1998;Long and Li, 2008;Papari et al, 2007;Petkov and Westenberg, 2003;Ren, 2008;Tang et al, 2007;Ursino and La Cara, 2004). Typically, Grigorescu et al (2003) and Petkov and Westenberg (2003) proposed two models, i.e., isotropic and anisotropic inhibition models, both employ a Gabor function to represent CRF and an annular surround area to simulate nCRF.…”

Section: Introductionmentioning

confidence: 99%

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Center–surround interaction with adaptive inhibition: A computational model for contour detection

Zeng

2011

NeuroImage

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Section: Discussionmentioning

confidence: 99%

Section: Other Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 96%

Section: Introductionmentioning

confidence: 99%

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Center–surround interaction with adaptive inhibition: A computational model for contour detection

Zeng

2011

NeuroImage

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“…The initial objective of this research was to make the diagnosis of the small bowel diseases directly, but the results were disappointing because of the complexity and the multiformity of the lesions. The complicated algorithm (Hidded Markov models, HMM [24,25] or artificial neural network, ANN [26][27][28] ) was of a tryout. Though the diagnosis accuracy could be improved to some extent, the result was not satisfactory to the physicians, and the scanning time of IPS was much longer than that of the readers' performance; therefore, it could not be accepted in the clinical practice.…”

Section: Discussionmentioning

confidence: 99%

A feasibility trial of computer-aided diagnosis for enteric lesions in capsule endoscopy

Gan¹,

Wu²,

Rao³

et al. 2008

WJG

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images for the rarely-encountered lesions were difficult to differentiate from the normal images. However, the number of the images screened by IPS was 5000 on average, and only 10%-15% of the original images were left behind. As a result, a large number of normal images were excluded, and the reading time decreased from 5 h to 1 h on average. CONCLUSION: Though the total accuracy and specificity rates by the computer-aided screening for the enteric lesions with IPS are much lower than those by the CE readers, the computer-aided screening diagnosis can exclude a large number of the normal images and confine the enteric lesions to 5000 images on average, which can reduce the workload of the readers in the scanning of the images. This computer-aided screening technique can make a correct diagnosis as efficiently as possible in most of the patients. INTRODUCTIONDepiction of the small intestines has still been a challenge because of their length and tortuosity. In the past time when some lesions occurred in an intestinal segment, they were usually difficult to identify because of a shortage of the efficiently used tools [1,2] . When the capsule endoscopy (CE) was introduced to the clinical practice in 2000, it became a revolutionary diagnostic tool in diagnosing small intestinal diseases [3] . Besides, there is growing evidence that CE may be used in diagnosing some diseases occurring in the colon and the esophagus [4,5] . Because of the incapability of its use in the insufflation, affusion, biopsy and therapy, when Abstract AIM: To investigate and evaluate the feasibility of the computer-aided screening diagnosis for enteric lesions in the capsule endoscopy (CE). METHODS: After developing a series of algorithms for the screening diagnosis of the enteric lesions in CE based on their characteristic colors and contours, the normal and abnormal images obtained from 289 patients were respectively scanned and diagnosed by the CE readers and by the computer-aided screening for the enteric lesions with the image-processed software (IPS). The enteric lesions shown by the images included esoenteritis, mucosal ulcer and erosion, bleeding, spaceoccupying lesions, angioectasia, diverticula, parasites, etc. The images for the lesions or the suspected lesions confirmed by the CE readers and the computers were collected, and the effectiveness rate of the screening and the number of the scanned images were evaluated, respectively. RESULTS: Compared with the diagnostic results obtained by the CE readers, the total effectiveness rate (sensitivity) in the screening of the commonlyencountered enteric lesions by IPS varied from 42.9% to 91.2%, with a median of 74.2%, though the specificity and the accuracy rates were still low, and the

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Shape Description and Recognition Method Inspired by the Primary Visual Cortex

Wei

2013

Cogn Comput

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A model of contour extraction including multiple scales, flexible inhibition and attention

Cited by 17 publications

References 82 publications

Center–surround interaction with adaptive inhibition: A computational model for contour detection

Center–surround interaction with adaptive inhibition: A computational model for contour detection

A feasibility trial of computer-aided diagnosis for enteric lesions in capsule endoscopy

Shape Description and Recognition Method Inspired by the Primary Visual Cortex

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