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Weber’s law for image feature descriptor (WLD) is based on the theory that the ratio of the increment threshold to the background intensity is a constant. It has been used in facial recognition, structure detection, and tissue classification in X-ray images. In this paper, WLD is explored in the polyp classification in color colonoscopy images for the first time. An open, on-line colonoscopy image database is used to evaluate the new descriptor. The database contains 74 polyps, including 19 benign polyps and 55 malignant ones. Each polyp has a white light image (WLI) and a narrow band image (NBI), both were obtained by the same fibro-colonoscopy from the same patient. WLD image texture features are extracted from three color channels of (1) color WLI, (2) color NBI and (3) WLI+NBI. The extracted features are analyzed, ranked and classified using a Random Forest package based on the merit of the area under the curve (AUC) of the Receiver Operating Characteristics (ROC). The performance of WLD is quantitatively documented by the AUC, the ROC curve, the P-R (precision-recall) plot and the accuracy measure with comparison to commonly used features, such as Haralick and local binary pattern feature descriptors. The results demonstrate the advantage of WLD in the polyp classification in terms of the quantitative measures.

Feature classification plays an important role in computer-aided diagnosis (CADx) of suspicious lesions. While many texture features have been extracted and applied for various clinical purposes, Haralick's feature extraction method is of great interest, because it gives a series of texture measures on the image intensity correlations among the image pixels across an image slice. Based on the Haralick's method, we proposed a new set of features for CADx of colonic polyps or differentiation of hyperplastic polyps from adenomas. We evaluated this new feature set by means of random forest (RF) classifiers on a database of 153 polyps, including 116 adenomas and 37 hyperplastic polyps. The classification results were documented quantitatively by the Receiver Operating Characteristics (ROC) analysis and the merit of area under the ROC curve (AUC), which are well-established evaluation criteria to various classifiers. Experimental results demonstrated that the new feature set significantly improved the CADx performance for colonic polyps.

An automatic method has been developed for segmentation of abdominal computed tomography (CT) images for virtual colonoscopy obtained after a bowel preparation of a low-residue diet with ingested contrast solutions to enhance the image intensities of residual colonic materials. Removal of the enhanced materials was performed electronically by a computer algorithm. The method is a multistage approach that employs a modified self-adaptive on-line, vector quantization technique for a low-level image classification and utilizes a region-growing strategy for a high-level feature extraction. The low-level classification labels each voxel based on statistical analysis of its three-dimensional intensity vectors consisting of nearby voxels. The high-level processing extracts the labeled stool, fluid and air voxels within the colon, and eliminates bone and lung voxels which have similar image intensities as the enhanced materials and air, but are physically separated from the colon. This method was evaluated by volunteer studies based on both objective and subjective criteria. The validation demonstrated that the method has a high reproducibility and repeatability and a small error due to partial volume effect. As a result of this electronic colon cleansing, routine physical bowel cleansing prior to virtual colonoscopy may not be necessary.

Computed tomography colonography (CTC) or CT-based virtual colonoscopy (VC) is an emerging tool for detection of colonic polyps. Compared to the conventional fiber-optic colonoscopy, VC has demonstrated the potential to become a mass screening modality in terms of safety, cost, and patient compliance. However, current CTC delivers excessive X-ray radiation to the patient during data acquisition. The radiation is a major concern for screening application of CTC. In this work, we performed a simulation study to demonstrate a possible ultra low-dose CT technique for VC. The ultra low-dose abdominal CT images were simulated by adding noise to the sinograms of the patient CTC images acquired with normal dose scans at 100 mA s levels. The simulated noisy sinogram or projection data were first processed by a Karhunen-Loeve domain penalized weighted least-squares (KL-PWLS) restoration method and then reconstructed by a filtered backprojection algorithm for the ultra low-dose CT images. The patient-specific virtual colon lumen was constructed and navigated by a VC system after electronic colon cleansing of the orally-tagged residue stool and fluid. By the KL-PWLS noise reduction, the colon lumen can successfully be constructed and the colonic polyp can be detected in an ultra low-dose level below 50 mA s. Polyp detection can be found more easily by the KL-PWLS noise reduction compared to the results using the conventional noise filters, such as Hanning filter. These promising results indicate the feasibility of an ultra low-dose CTC pipeline for colon screening with less-stressful bowel preparation by fecal tagging with oral contrast.

Computed tomography (CT)-based virtual colonoscopy or CT colonography (CTC) currently utilizes oral contrast solutions to tag the colonic fluid and possibly residual stool for differentiation from the colon wall and polyps. The enhanced image density of the tagged colonic materials causes a significant partial volume (PV) effect into the colon wall as well as the lumen space (filled with air or CO 2 ). The PV effect on the colon wall can "bury" polyps of size as large as 5mm by increasing their image densities to a noticeable level, resulting in false negatives. It can also create false positives when PV effect goes into the lumen space. We have been modeling the PV effect for mixture-based image segmentation and developing text-based computer-aided detection of polyp (CADpolyp) by utilizing the PV mixture-based image segmentation. This work presents some preliminary results of developing and applying texture-based CADpolyp technique to low-dose CTC studies. A total of 114 studies of asymptomatic patients older than 50, who underwent CTC and then optical colonoscopy (OC) on the same day, were selected from a database, which was accumulated in the past decade and contains various bowel preparations and CT scanning protocols. The participating radiologists found ten polyps of greater than 5 mm from a total of 16 OC proved polyps, i.e., a detection sensitivity of 63%. They scored 23 false positives from the database, i.e., a 20% false positive rate. Approximately 70% of the datasets were marked as imperfect bowel cleansing and/or presence of image artifacts. The impact of imperfect bowel cleansing and image artifacts on VC performance is significant. The texture-based CADpolyp detected all the polyps with an average of 2.68 false positives per patient. This indicates that texture-based CADpolyp can improve the CTC performance in the cases of imperfect cleansed bowels and presence of image artifacts.

Computed tomography colonography (CTC) or CT- based virtual colonoscopy (VC) is an emerging tool for detection of colonic polyps. Compared to the conventional fiber-optic colonoscopy, VC has demonstrated the potential to become a mass screening modality in terms of safety, cost, and patient compliance. However, current CTC delivers excessive X-ray radiation to the patient during data acquisition. The radiation dose is a major concern for screening application of CTC. In this work, we performed a simulation study to demonstrate a possible ultra low-dose CT technique for VC. The ultra low-dose abdominal CT images were simulated by adding noise to the sinograms of the patient CTC images acquired with normal dose scans at 100 mAs levels. The simulated noisy sinogram or projection data were first processed by a Karhunen-Loeve domain penalized weighted least squares (KL-PWLS) restoration method and then reconstructed by a filtered backprojection algorithm for the ultra low-dose CT images. The patient-specific virtual colon lumen was constructed and navigated by a VC system after electronic colon cleansing of the orally-tagged residue stool and fluid. By the KL-PWLS noise reduction, the colon lumen can be successfully constructed and the colonic polyp can be detected in an ultra low-dose level below 50 mAs. Polyp detection was also found easier by the KL-PWLS noise reduction compared to the results using the conventional noise filters, such as Hanning filter. These promising results indicate the feasibility of an ultra low-dose CTC pipeline for colon screening.

Electronic colon cleansing (ECC) aims to segment the colon lumen from a patient abdominal image acquired using an oral contrast agent for colonic material tagging, so that a virtual colon model can be constructed. Virtual colonoscopy (VC) provides fly-through navigation within the colon model, looking for polyps on the inner surface in a manner analogous to that of fiber optic colonoscopy. We have built an ECC pipeline for a commercial VC navigation system. In this paper, we present an improved ECC method. It is based on a partial-volume (PV) image-segmentation framework, which is derived using the well-established statistical expectation-maximization algorithm. The presented ECC method was evaluated by both visual inspection and computer-aided detection of polyps (CADpolyp) within the cleansed colon lumens obtained using 20 patient datasets. Compared to our previous ECC pipeline, which does not sufficiently consider the PV effect, the method presented in this paper demonstrates improved polyp detection by both visual judgment and CADpolyp measure.

Objective: To investigate a less stressful bowel preparation for polyp screening by virtual colonoscopy (VC) with follow-up biopsy on the positive findings by optical colonoscopy (OC). Materials and Methods: Fifty-eight volunteers of age older than 40 -- receiving low-residue diet and laxatives of magnesium citrate, bisacodyl tablets and suppository -- were divided into three groups. In Group I, 16 volunteers took three 40cc oral doses of MD-Gastroview with the three meals respectively, the day prior to VC procedure. In Group II, 18 volunteers ingested barium sulfate suspension (2% w/v, 250 cc/dose) at bedtime and in the next day morning of VC. In Group III, 24 volunteers received 60 cc of MD-Gastroview at bedtime and in the next day morning of VC. Following colon inflation with CO2, computer tomography (CT) abdominal images were acquired by a standard single-slice detector-band VC protocol, i.e., 5 mm collimation, 1 mm reconstruction, 1.5-2.0:1.0 pitch, 120 kVp and 100-150 mA. The CT density of the tagged residual fluid was measured. An image segmentation algorithm was applied to remove electronically the residue fluid. Results: The average fluid density was 97 HU for Group I, 221 HU for Group II2, and 599 HU for Group III. These three groups" density means are significantly different (p < 0.001 one-way ANOVA). After the electronic cleansing, the % of cleansed fluid regions was 5.5%, 16.5% and 93.1% (p