Computer-aided detection and diagnosis of colon polyps with morphological and texture features

Wang, Zigang; Li, Lihong; Anderson, Joseph C.; Harrington, Donald P.; Zhang, Liang

doi:10.1117/12.535664

Cited by 15 publications

(8 citation statements)

References 13 publications

(6 reference statements)

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“…Based on the observation [9,42,46], the density values within a polyp change gradually and smoothly from the center to its border. This attribute is reflected by the convergence of the triple-element vectors toward the corner (1.0, 1.0, 1.0) in the 3D presentation of Figure 7(d).…”

Section: Extracting Features From the Constructed Eroimentioning

confidence: 99%

Reduction of false positives by internal features for polyp detection in CT‐based virtual colonoscopy

Wang

Zhang

et al. 2005

Medical Physics

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In this paper, we present a computer-aided detection (CAD) method to extract and use internal features to reduce false positive (FP) rate generated by surface-based measures on the inner colon wall in computed tomographic (CT) colonography. Firstly, a new shape description global curvature, which can provide an overall shape description of the colon wall, is introduced to improve the detection of suspicious patches on the colon wall whose geometrical features are similar to that of the colonic polyps. By a ray-driven edge finder, the volume of each detected patch is extracted as a fitted ellipsoid model. Within the ellipsoid model, CT image density distribution is analyzed. Three types of (geometrical, morphological and textural) internal features are extracted and applied to eliminate the FPs from the detected patches. The presented CAD method was tested by a total of 153 patient datasets in which 45 patients were found with 61 polyps of sizes 4-30 mm by optical colonoscopy. For a 100% detection sensitivity (on polyps), the presented CAD method had an average FPs of 2.68 per patient dataset and eliminated 93.1% of FPs generated by the surface-based measures. The presented CAD method was also evaluated by different polyp sizes. For polyp sizes of 10-30 mm, the method achieved mean number of FPs per dataset of 2.0 with 100% sensitivity. For polyp sizes of 4-10 mm, the method achieved 3.44 FP per dataset with 100% sensitivity.

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Section: Extracting Features From the Constructed Eroimentioning

confidence: 99%

Reduction of false positives by internal features for polyp detection in CT‐based virtual colonoscopy

Wang

Zhang

et al. 2005

Medical Physics

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“…Further improvement of the image processing strategy is needed to relax the food restriction in the protocols. Texture analysis on the tagged colonic materials is needed to differentiate the false positives from polyps [35][36][37]. The partial volume effect due to the enhanced image density of colonic materials could be reduced by alternative oral contrasts, which will decrease, rather than increase, the image density of the colonic materials towards that of the air in the lumen.…”

Section: Resultsmentioning

confidence: 98%

A feasibility study on laxative-free bowel preparation for virtual colonoscopy

et al. 2005

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Objective:To investigate the feasibility of laxative-free bowel preparation to relieve the patient stress in colon cleansing for virtual colonoscopy. Materials and Methods: Three different bowel-preparation protocols were investigated by 60 study cases from 35 healthy male volunteers. All the protocols utilize low-residue diet for two days and differ in diet for the third day -the day just prior to image acquisition in the fourth day morning. Protocol Diet-1 utilizes fluid or liquid diet in the third day, Diet-2 utilizes a food kit, and Diet-3 remains the low-residue diet. Oral contrast of barium sulfate (2.1%, 250 ml) was added respectively to the dinner in the second day and the three meals in the third day. Two doses of MD-Gastroview (60 ml) were ingested each in the evening of the third day and in the morning before image acquisition. Images were acquired by a single-slice detector spiral CT (computed tomography) scanner with 5 mm collimation, 1 mm reconstruction, 1.5-2.0:1.0 pitch, 100-150 mA, and 120 kVp after the colons were inflated by CO 2 . The contrasted colonic residue materials were electronically removed from the CT images by specialized computer-segmentation algorithms. Results: By assumptions that the healthy young volunteers have no polyp and the image resolution is approximately 4 mm, a successful electronic cleansing is defined as "no more than five false positives and no removal of a colon fold part greater than 4 mm" for each study case. The successful rate is 100% for protocol Diet-1, 77% for Diet-2 and 57% for Diet-3. Conclusion: A laxative-free bowel preparation is feasible for virtual colonoscopy.

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“…Given an extracted mucosa layer, our previously developed surface-based [9] and texture-based [10] CAD techniques are used to analyze the extracted mucosa layer for polyp detection. The CADpolyp result from this presented ECC method is compared to that of our previous ECC pipeline.…”

Section: Cleansing the Colon Lumen And Extracting The Mucosa Layermentioning

confidence: 99%

“…The results are shown in Table 1. In addition to the surface-based CAD, we also applied our texture -based CAD to the cleansed colons [10]. This texture -based CAD utilizes additional morphological and texture information of the detected suspect to reduce the false positives.…”

Section: Cleansing the Colon Lumen And Extracting The Mucosa Layermentioning

confidence: 99%

An Improved Electronic Colon Cleansing Method for Detection of Polyps by Virtual Colonoscopy

Wang

et al. 2005

2005 IEEE Engineering in Medicine and Biology 27th Annual Conference

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Electronic colon cleansing (ECC) aims to segment the colon lumen from the patient abdominal image acquired with colonic material tagging by oral contrast and other means, so that a virtual colon model can be constructed. Virtual colonoscopy (VC) navigates through the colon model looking for polyps in a similar manner as the fiber optic colonoscopy does. We had built an ECC pipeline for the commercial VC system of Viatronix Inc. In this paper, we present an improved ECC method. It is based on a partial -volume image -segmentation framework, which is derived using the well-established statistical expectation-maximization algorithm. The presented ECC method was evaluated by both visual inspection on the cleansed colon lumens and computer-aided detection of polyps (CADpolyp) using 20 patient datasets. Compared to our previous ECC pipeline, this presented new method demonstrates improvement in both visual judgment and CADpolyp.

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Computer-aided detection and diagnosis of colon polyps with morphological and texture features

Cited by 15 publications

References 13 publications

Reduction of false positives by internal features for polyp detection in CT‐based virtual colonoscopy

Reduction of false positives by internal features for polyp detection in CT‐based virtual colonoscopy

A feasibility study on laxative-free bowel preparation for virtual colonoscopy

An Improved Electronic Colon Cleansing Method for Detection of Polyps by Virtual Colonoscopy

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