Toru Tamaki

Computer-aided colorectal tumor classification in NBI endoscopy using local features

An early detection of colorectal cancer through colorectal endoscopy is important and widely used in hospitals as a standard medical procedure. During colonoscopy, the lesions of colorectal tumors on the colon surface are visually inspected by a Narrow Band Imaging (NBI) zoom-videoendoscope. By using the visual appearance of colorectal tumors in endoscopic images, histological diagnosis is presumed based on classification schemes for NBI magnification findings. In this paper, we report on the performance of a recognition system for classifying NBI images of colorectal tumors into three types (A, B, and C3) based on the NBI magnification findings. To deal with the problem of computer-aided classification of NBI images, we explore a local feature-based recognition method, bag-of-visual-words (BoW), and provide extensive experiments on a variety of technical aspects. The proposed prototype system, used in the experiments, consists of a bag-of-visual-words representation of local features followed by Support Vector Machine (SVM) classifiers. A number of local features are extracted by using sampling schemes such as Difference-of-Gaussians and grid sampling. In addition, in this paper we propose a new combination of local features and sampling schemes. Extensive experiments with varying the parameters for each component are carried out, for the performance of the system is usually affected by those parameters, e.g. the sampling strategy for the local features, the representation of the local feature histograms, the kernel types of the SVM classifiers, the number of classes to be considered, etc. The recognition results are compared in terms of recognition rates, precision/recall, and F-measure for different numbers of visual words. The proposed system achieves a recognition rate of 96% for 10-fold cross validation on a real dataset of 908 NBI images collected during actual colonoscopy, and 93% for a separate test dataset.

Computer-aided system for predicting the histology of colorectal tumors by using narrow-band imaging magnifying colonoscopy (with video)

BACKGROUND Narrow-band imaging (NBI) classification of colorectal lesions is clinically useful in determining treatment options for colorectal tumors. There is a learning curve, however. Accurate NBI-based diagnosis requires training and experience. In addition, objective diagnosis is necessary. Thus, we developed a computerized system to automatically classify NBI magnifying colonoscopic images. OBJECTIVE To evaluate the utility and limitations of our automated NBI classification system. DESIGN Retrospective study. SETTING Department of endoscopy, university hospital. MAIN OUTCOME MEASUREMENTS Performance of our computer-based system for classification of NBI magnifying colonoscopy images in comparison to classification by two experienced endoscopists and to histologic findings. RESULTS For the 371 colorectal lesions depicted on validation images, the computer-aided classification system yielded a detection accuracy of 97.8% (363/371); sensitivity and specificity of types B-C3 lesions for a diagnosis of neoplastic lesion were 97.8% (317/324) and 97.9% (46/47), respectively. Diagnostic concordance between the computer-aided classification system and the two experienced endoscopists was 98.7% (366/371), with no significant difference between methods. LIMITATIONS Retrospective, single-center in this initial report. CONCLUSION Our new computer-aided system is reliable for predicting the histology of colorectal tumors by using NBI magnifying colonoscopy.

Softassign and EM-ICP on GPU

In this paper we propose CUDA-based implementations of two 3D point sets registration algorithms: Soft assign and EM-ICP. Both algorithms are known for being time demanding, even on modern multi-core CPUs. Our GPUbased implementations vastly outperform CPU ones. For instance, our CUDA EM-ICP aligns 5000 points in less than 7 seconds on a GeForce 8800GT, while the same implementation in OpenMP on an Intel Core 2 Quad would take 7 minutes.

A computer system to be used with laser-based endoscopy for quantitative diagnosis of early gastric cancer.

Goals: To evaluate the usefulness of a newly devised computer system for use with laser-based endoscopy in differentiating between early gastric cancer, reddened lesions, and surrounding tissue. Background: Narrow-band imaging based on laser light illumination has come into recent use. We devised a support vector machine (SVM)-based analysis system to be used with the newly devised endoscopy system to quantitatively identify gastric cancer on images obtained by magnifying endoscopy with blue-laser imaging (BLI). We evaluated the usefulness of the computer system in combination with the new endoscopy system. Study: We evaluated the system as applied to 100 consecutive early gastric cancers in 95 patients examined by BLI magnification at Hiroshima University Hospital. We produced a set of images from the 100 early gastric cancers; 40 flat or slightly depressed, small, reddened lesions; and surrounding tissues, and we attempted to identify gastric cancer, reddened lesions, and surrounding tissue quantitatively. Results: The average SVM output value was 0.846±0.220 for cancerous lesions, 0.381±0.349 for reddened lesions, and 0.219±0.277 for surrounding tissue, with the SVM output value for cancerous lesions being significantly greater than that for reddened lesions or surrounding tissue. The average SVM output value for differentiated-type cancer was 0.840±0.207 and for undifferentiated-type cancer was 0.865±0.259. Conclusions: Although further development is needed, we conclude that our computer-based analysis system used with BLI will identify gastric cancers quantitatively.

Unified approach to image distortion

We propose a new unified approach to deal with two-formulations of image distortion and a method for estimating the distortion parameters by using the both formulations; the two formulations have been developed separately. The proposed method is based on image registration and consists of nonlinear optimization to estimate parameters including view change and radial distortion. Experimental results demonstrate that our approach works well for both formulations.

Directional wavelet based features for colonic polyp classification

In this work, various wavelet based methods like the discrete wavelet transform, the dual-tree complex wavelet transform, the Gabor wavelet transform, curvelets, contourlets and shearlets are applied for the automated classification of colonic polyps. The methods are tested on 8 HD-endoscopic image databases, where each database is acquired using different imaging modalities (Pentaxs i-Scan technology combined with or without staining the mucosa), 2 NBI high-magnification databases and one database with chromoscopy high-magnification images. To evaluate the suitability of the wavelet based methods with respect to the classification of colonic polyps, the classification performances of 3 wavelet transforms and the more recent curvelets, contourlets and shearlets are compared using a common framework. Wavelet transforms were already often and successfully applied to the classification of colonic polyps, whereas curvelets, contourlets and shearlets have not been used for this purpose so far. We apply different feature extraction techniques to extract the information of the subbands of the wavelet based methods. Most of the in total 25 approaches were already published in different texture classification contexts. Thus, the aim is also to assess and compare their classification performance using a common framework. Three of the 25 approaches are novel. These three approaches extract Weibull features from the subbands of curvelets, contourlets and shearlets. Additionally, 5 state-of-the-art non wavelet based methods are applied to our databases so that we can compare their results with those of the wavelet based methods. It turned out that extracting Weibull distribution parameters from the subband coefficients generally leads to high classification results, especially for the dual-tree complex wavelet transform, the Gabor wavelet transform and the Shearlet transform. These three wavelet based transforms in combination with Weibull features even outperform the state-of-the-art methods on most of the databases. We will also show that the Weibull distribution is better suited to model the subband coefficient distribution than other commonly used probability distributions like the Gaussian distribution and the generalized Gaussian distribution. So this work gives a reasonable summary of wavelet based methods for colonic polyp classification and the huge amount of endoscopic polyp databases used for our experiments assures a high significance of the achieved results.

A preliminarily study for predicting body weight and milk properties in lactating Holstein cows using a three-dimensional camera system

Digital imaging has been applied to assess body weight and fatness in livestock.We examine low priced 3D camera for estimating cow body weight and milk properties.Six geodesic line (GL) lengths were computed using back posture 3D object of cow.A similar determination of body condition with standard method is possible. Since manual body condition scoring has been widely utilized as an indirect and subjective method to estimate energy reserves of dairy cattle, image analysis has been increasingly researched for use on large farms as an objective and effective measuring instrument for the estimation of body condition score (BCS) and body weight (BW). Recent advances in the technological development of the three-dimensional (3D) cameras may provide innovative feed management tools for dairy farms. The objective of the present study was to evaluate the feasibility of a 3D camera systems in measuring the back posture of lactating Holstein dairy cows to predict the BCS, BW, milk yield (MY), milk fat (MF) and milk protein (MP). The BCSs for eight cows were recorded by two trained observers using a 5-point scale, and other variables were obtained using an automatic milking system during the lactation. Back posture measurements of dairy cows were conducted using the ASUS Xtion Pro sensor. Six geodesic line (GL) lengths were computed using the 3D objects of each cow based on the positions of the right and left hook bones (GLhh), right and left thurl bones (GLtt), right and left pin bones (GLpp), hook and thurl bones (GLht), hook and pin bones (GLhp), and coccygeal ligament (GLcl). In the principal component analysis (PCA), GL, GLpp, and GLcl had the greatest contribution to principal component values (PCV) 1, 2, and 3, respectively, and these three PCVs described 0.887 of the cumulative contribution ratio. Good correlations were found between the observed and predicted values of BCS (R2=0.74), BW (0.80), MY (0.62), MF (0.62), and MP (0.53) based on linear regression equations using the GLs as explanatory variables and parity (1, 2, and >3) as a fixed effect. These results demonstrate that the 3D cameras could represent an innovative tool for estimating body condition and milk properties.

A system for colorectal tumor classification in magnifying endoscopic NBI images

In this paper we propose a recognition system for classifying NBI images of colorectal tumors into three types (A, B, and C3) of structures of microvessels on the colorectal surface. These types have a strong correlation with histologic diagnosis: hyperplasias (HP), tubular adenomas (TA), and carcinomas with massive submucosal invasion (SM-m). Images are represented by Bag-of-features of the SIFT descriptors densely sampled on a grid, and then classified by an SVM with an RBF kernel. A dataset of 907 NBI images were used for experiments with 10-fold cross-validation, and recognition rate of 94.1% were obtained.

Quantitative analysis and development of a computer-aided system for identification of regular pit patterns of colorectal lesions

BACKGROUND Because pit pattern classification of colorectal lesions is clinically useful in determining treatment options for colorectal tumors but requires extensive training, we developed a computerized system to automatically quantify and thus classify pit patterns depicted on magnifying endoscopy images. OBJECTIVE To evaluate the utility and limitations of our automated pit pattern classification system. DESIGN Retrospective study. SETTING Department of endoscopy at a university hospital. MAIN OUTCOME MEASUREMENTS Performance of our automated computer-based system for classification of pit patterns on magnifying endoscopic images in comparison to classification by diagnosis of the 134 regular pit pattern images by an endoscopist. RESULTS For type I and II pit patterns, the results of discriminant analysis were in complete agreement with the endoscopic diagnoses. Type IIIl was diagnosed in 29 of 30 cases (96.7%) and type IV was diagnosed in 1 case. Twenty-nine of 30 cases (96.7%) were diagnosed as type IV pit pattern. The overall accuracy of our computerized recognition system was 132 of 134 (98.5%). CONCLUSIONS Our system is best characterized as semiautomated but is a step toward the development of a fully automated system to assist in the diagnosis of colorectal lesions based on classification of pit patterns.

Local fractal dimension based approaches for colonic polyp classification

This work introduces texture analysis methods that are based on computing the local fractal dimension (LFD; or also called the local density function) and applies them for colonic polyp classification. The methods are tested on 8 HD-endoscopic image databases, where each database is acquired using different imaging modalities (Pentaxs i-Scan technology combined with or without staining the mucosa) and on a zoom-endoscopic image database using narrow band imaging. In this paper, we present three novel extensions to a LFD based approach. These extensions additionally extract shape and/or gradient information of the image to enhance the discriminativity of the original approach. To compare the results of the LFD based approaches with the results of other approaches, five state of the art approaches for colonic polyp classification are applied to the employed databases. Experiments show that LFD based approaches are well suited for colonic polyp classification, especially the three proposed extensions. The three proposed extensions are the best performing methods or at least among the best performing methods for each of the employed databases. The methods are additionally tested by means of a public texture image database, the UIUCtex database. With this database, the viewpoint invariance of the methods is assessed, an important features for the employed endoscopic image databases. Results imply that most of the LFD based methods are more viewpoint invariant than the other methods. However, the shape, size and orientation adapted LFD approaches (which are especially designed to enhance the viewpoint invariance) are in general not more viewpoint invariant than the other LFD based approaches.