Engineering topics

Nishikawa, Robert M.

Publications and source records attributed to Nishikawa, Robert M..

5 records

DOE OSTI2015

Local curvature analysis for classifying breast tumors: Preliminary analysis in dedicated breast CT

Purpose: The purpose of this study is to measure the effectiveness of local curvature measures as novel image features for classifying breast tumors. Methods: A total of 119 breast lesions from 104 noncontrast dedicated breast computed tomography images of women were used in this study. Volumetric segmentation was done using a seed-based segmentation algorithm and then a triangulated surface was extracted from the resulting segmentation. Total, mean, and Gaussian curvatures were then computed. Normalized curvatures were used as classification features. In addition, traditional image features were also extracted and a forward feature selection scheme was used to select the optimal feature set. Logistic regression was used as a classifier and leave-one-out cross-validation was utilized to evaluate the classification performances of the features. The area under the receiver operating characteristic curve (AUC, area under curve) was used as a figure of merit. Results: Among curvature measures, the normalized total curvature (C{sub T}) showed the best classification performance (AUC of 0.74), while the others showed no classification power individually. Five traditional image features (two shape, two margin, and one texture descriptors) were selected via the feature selection scheme and its resulting classifier achieved an AUC of 0.83. Among those five features, the radial gradient index (RGI), which is a margin descriptor, showed the best classification performance (AUC of 0.73). A classifier combining RGI and C{sub T} yielded an AUC of 0.81, which showed similar performance (i.e., no statistically significant difference) to the classifier with the above five traditional image features. Additional comparisons in AUC values between classifiers using different combinations of traditional image features and C{sub T} were conducted. The results showed that C{sub T} was able to replace the other four image features for the classification task. Conclusions: The normalized curvature measure contains useful information in classifying breast tumors. Using this, one can reduce the number of features in a classifier, which may result in more robust classifiers for different datasets.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2015

A computational model to generate simulated three-dimensional breast masses

Purpose: To develop algorithms for creating realistic three-dimensional (3D) simulated breast masses and embedding them within actual clinical mammograms. The proposed techniques yield high-resolution simulated breast masses having randomized shapes, with user-defined mass type, size, location, and shape characteristics. Methods: The authors describe a method of producing 3D digital simulations of breast masses and a technique for embedding these simulated masses within actual digitized mammograms. Simulated 3D breast masses were generated by using a modified stochastic Gaussian random sphere model to generate a central tumor mass, and an iterative fractal branching algorithm to add complex spicule structures. The simulated masses were embedded within actual digitized mammograms. The authors evaluated the realism of the resulting hybrid phantoms by generating corresponding left- and right-breast image pairs, consisting of one breast image containing a real mass, and the opposite breast image of the same patient containing a similar simulated mass. The authors then used computer-aided diagnosis (CAD) methods and expert radiologist readers to determine whether significant differences can be observed between the real and hybrid images. Results: The authors found no statistically significant difference between the CAD features obtained from the real and simulated images of masses with either spiculated or nonspiculated margins. Likewise, the authors found that expert human readers performed very poorly in discriminating their hybrid images from real mammograms. Conclusions: The authors’ proposed method permits the realistic simulation of 3D breast masses having user-defined characteristics, enabling the creation of a large set of hybrid breast images containing a well-characterized mass, embedded within real breast background. The computational nature of the model makes it suitable for detectability studies, evaluation of computer aided diagnosis algorithms, and teaching purposes.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2014

Analysis of perceived similarity between pairs of microcalcification clusters in mammograms

Purpose: Content-based image retrieval aims to assist radiologists by presenting example images with known pathology that are visually similar to the case being evaluated. In this work, the authors investigate several fundamental issues underlying the similarity ratings between pairs of microcalcification (MC) lesions on mammograms as judged by radiologists: the degree of variability in the similarity ratings, the impact of this variability on agreement between readers in retrieval of similar lesions, and the factors contributing to the readers’ similarity ratings. Methods: The authors conduct a reader study on a set of 1000 image pairs of MC lesions, in which a group of experienced breast radiologists rated the degree of similarity between each image pair. The image pairs are selected, from among possible pairings of 222 cases (110 malignant, 112 benign), based on quantitative image attributes (features) and the results of a preliminary reader study. Next, the authors apply analysis of variance (ANOVA) to quantify the level of variability in the readers’ similarity ratings, and study how the variability in individual reader ratings affects consistency between readers. The authors also measure the extent to which readers agree on images which are most similar to a given query, for which the Dice coefficient is used. To investigate how the similarity ratings potentially relate to the attributes underlying the cases, the authors study the fraction of perceptually similar images that also share the same benign or malignant pathology as the query image; moreover, the authors apply multidimensional scaling (MDS) to embed the cases according to their mutual perceptual similarity in a two-dimensional plot, which allows the authors to examine the manner in which similar lesions relate to one another in terms of benign or malignant pathology and clustered MCs. Results: The ANOVA results show that the coefficient of determination in the reader similarity ratings is 0.59. The variability level in the similarity ratings is proved to be a limiting factor, leading to only moderate correlation between the readers in their readings. The Dice coefficient, measuring agreement between readers in retrieval of similar images, can vary from 0.45 to 0.64 with different levels of similarity for individual readers, but is higher for average ratings from a group of readers (from 0.59 to 0.78). More importantly, the fraction of retrieved cases that match the benign or malignant pathology of the query image was found to increase with the degree of similarity among the retrieved images, reaching average value as high as 0.69 for the radiologists (p-value <10{sup −4} compared to random guessing). Moreover, MDS embedding of all the cases shows that cases having the same pathology tend to cluster together, and that neighboring cases in the plot tend to be similar in their clustered MCs. Conclusions: While individual readers exhibit substantial variability in their similarity ratings, similarity ratings averaged from a group of readers can achieve a high level of intergroup consistency and agreement in retrieval of similar images. More importantly, perceptually similar cases are also likely to be similar in their underlying benign or malignant pathology and image features of clustered MCs, which could be of diagnostic value in computer-aided diagnosis for lesions with clustered MCs.

62 RADIOLOGY AND NUCLEAR MEDICINE↗

DOE OSTI2013

Algorithmic scatter correction in dual-energy digital mammography

Purpose: Small calcifications are often the earliest and the main indicator of breast cancer. Dual-energy digital mammography (DEDM) has been considered as a promising technique to improve the detectability of calcifications since it can be used to suppress the contrast between adipose and glandular tissues of the breast. X-ray scatter leads to erroneous calculations of the DEDM image. Although the pinhole-array interpolation method can estimate scattered radiations, it requires extra exposures to measure the scatter and apply the correction. The purpose of this work is to design an algorithmic method for scatter correction in DEDM without extra exposures.Methods: In this paper, a scatter correction method for DEDM was developed based on the knowledge that scattered radiation has small spatial variation and that the majority of pixels in a mammogram are noncalcification pixels. The scatter fraction was estimated in the DEDM calculation and the measured scatter fraction was used to remove scatter from the image. The scatter correction method was implemented on a commercial full-field digital mammography system with breast tissue equivalent phantom and calcification phantom. The authors also implemented the pinhole-array interpolation scatter correction method on the system. Phantom results for both methods are presented and discussed. The authors compared the background DE calcification signals and the contrast-to-noise ratio (CNR) of calcifications in the three DE calcification images: image without scatter correction, image with scatter correction using pinhole-array interpolation method, and image with scatter correction using the authors' algorithmic method.Results: The authors' results show that the resultant background DE calcification signal can be reduced. The root-mean-square of background DE calcification signal of 1962 μm with scatter-uncorrected data was reduced to 194 μm after scatter correction using the authors' algorithmic method. The range of background DE calcification signals using scatter-uncorrected data was reduced by 58% with scatter-corrected data by algorithmic method. With the scatter-correction algorithm and denoising, the minimum visible calcification size can be reduced from 380 to 280 μm.Conclusions: When applying the proposed algorithmic scatter correction to images, the resultant background DE calcification signals can be reduced and the CNR of calcifications can be improved. This method has similar or even better performance than pinhole-array interpolation method in scatter correction for DEDM; moreover, this method is convenient and requires no extra exposure to the patient. Although the proposed scatter correction method is effective, it is validated by a 5-cm-thick phantom with calcifications and homogeneous background. The method should be tested on structured backgrounds to more accurately gauge effectiveness.

62 RADIOLOGY AND NUCLEAR MEDICINE↗

DOE OSTI2012

A statistically defined anthropomorphic software breast phantom

Purpose: Digital anthropomorphic breast phantoms have emerged in the past decade because of recent advances in 3D breast x-ray imaging techniques. Computer phantoms in the literature have incorporated power-law noise to represent glandular tissue and branching structures to represent linear components such as ducts. When power-law noise is added to those phantoms in one piece, the simulated fibroglandular tissue is distributed randomly throughout the breast, resulting in dense tissue placement that may not be observed in a real breast. The authors describe a method for enhancing an existing digital anthropomorphic breast phantom by adding binarized power-law noise to a limited area of the breast. Methods: Phantoms with (0.5 mm){sup 3} voxel size were generated using software developed by Bakic et al. Between 0% and 40% of adipose compartments in each phantom were replaced with binarized power-law noise ({beta} = 3.0) ranging from 0.1 to 0.6 volumetric glandular fraction. The phantoms were compressed to 7.5 cm thickness, then blurred using a 3 Multiplication-Sign 3 boxcar kernel and up-sampled to (0.1 mm){sup 3} voxel size using trilinear interpolation. Following interpolation, the phantoms were adjusted for volumetric glandular fraction using global thresholding. Monoenergetic phantom projections were created, including quantum noise and simulated detector blur. Texture was quantified in the simulated projections using power-spectrum analysis to estimate the power-law exponent {beta} from 25.6 Multiplication-Sign 25.6 mm{sup 2} regions of interest. Results: Phantoms were generated with total volumetric glandular fraction ranging from 3% to 24%. Values for {beta} (averaged per projection view) were found to be between 2.67 and 3.73. Thus, the range of textures of the simulated breasts covers the textures observed in clinical images. Conclusions: Using these new techniques, digital anthropomorphic breast phantoms can be generated with a variety of glandular fractions and patterns. {beta} values for this new phantom are comparable with published values for breast tissue in x-ray projection modalities. The combination of conspicuous linear structures and binarized power-law noise added to a limited area of the phantom qualitatively improves its realism.

60 APPLIED LIFE SCIENCES↗