Journal of Medical Imaging

Giger, Maryellen L.

2014 Journal of Medical Imaging

doi: 10.1117/1.JMI.1.1.010101pmid: 26158020

Veiga, Diana; Pereira, Carla; Ferreira, Manuel; Gonçalves, Luís; Monteiro, João

2014 Journal of Medical Imaging

doi: 10.1117/1.JMI.1.1.014001pmid: 26158021

Abstract.The evaluation of image quality is an important step before an automatic analysis of retinal images. Several conditions can impair the acquisition of a good image, and minimum image quality requirements should be present to ensure that an automatic or semiautomatic system provides an accurate diagnosis. A method to classify fundus images as low or good quality is presented. The method starts with the detection of regions of uneven illumination and evaluates if the segmented noise masks affect a clinically relevant area (around the macula). Afterwards, focus is evaluated through a fuzzy classifier. An input vector is created extracting three focus features. The system was validated in a large dataset (1454 fundus images), obtained from an online database and an eye clinic and compared with the ratings of three observers. The system performance was close to optimal with an area under the receiver operating characteristic curve of 0.9943.

Khademi, April; Venetsanopoulos, Anastasios; Moody, Alan R.

2014 Journal of Medical Imaging

doi: 10.1117/1.JMI.1.1.014002pmid: 26158022

Abstract.An artifact found in magnetic resonance images (MRI) called partial volume averaging (PVA) has received much attention since accurate segmentation of cerebral anatomy and pathology is impeded by this artifact. Traditional neurological segmentation techniques rely on Gaussian mixture models to handle noise and PVA, or high-dimensional feature sets that exploit redundancy in multispectral datasets. Unfortunately, model-based techniques may not be optimal for images with non-Gaussian noise distributions and/or pathology, and multispectral techniques model probabilities instead of the partial volume (PV) fraction. For robust segmentation, a PV fraction estimation approach is developed for cerebral MRI that does not depend on predetermined intensity distribution models or multispectral scans. Instead, the PV fraction is estimated directly from each image using an adaptively defined global edge map constructed by exploiting a relationship between edge content and PVA. The final PVA map is used to segment anatomy and pathology with subvoxel accuracy. Validation on simulated and real, pathology-free T1 MRI (Gaussian noise), as well as pathological fluid attenuation inversion recovery MRI (non-Gaussian noise), demonstrate that the PV fraction is accurately estimated and the resultant segmentation is robust. Comparison to model-based methods further highlight the benefits of the current approach.

Mulder, Harriët W.; van Stralen, Marijn; van der Zwaan, Heleen B.; Leung, K. Y. Esther; Bosch, Johan G.; Pluim, Josien P. W.

2014 Journal of Medical Imaging

doi: 10.1117/1.JMI.1.1.014004pmid: 26158023

Abstract.Mosaicing of real-time three-dimensional echocardiography (RT3-DE) images aims at extending the field-of-view of overlapping images. Currently available methods discard most of the temporal information available in the time series. We investigate the added value of simultaneous registration of multiple temporal frames using common similarity metrics. We combine RT3-DE images of the left and right ventricles by registration and fusion. The standard approach of registering single frames, either end-diastolic (ED) or end-systolic (ES), is compared with simultaneous registration of multiple time frames, to evaluate the effect of using the information from all images in the metric. A transformation estimating the protocol-specific misalignment is used to initialize the registration. It is shown that multiframe registration can be as accurate as alignment of the images based on manual annotations. Multiframe registration using normalized cross-correlation outperforms any of the single-frame methods. As opposed to expectations, extending the multiframe registration beyond simultaneous use of ED and ES frames does not further improve registration results.

Koran, Mary Ellen; Thornton-Wells, Tricia A.; Jahanshad, Neda; Glahn, David C.; Thompson, Paul M.; Blangero, John; Nichols, Thomas E.; Kochunov, Peter; Landman, Bennett A.

2014 Journal of Medical Imaging

doi: 10.1117/1.JMI.1.1.014005pmid: 25558465

Abstract.Imaging genetics is an emerging methodological field that combines genetic information with medical imaging-derived metrics to understand how genetic factors impact observable phenotypes. In order for a trait to be a reasonable phenotype in an imaging genetics study, it must be heritable: at least some proportion of its variance must be due to genetic influences. The Sequential Oligogenic Linkage Analysis Routines (SOLAR) imaging genetics software can estimate the heritability of a trait in complex pedigrees. We investigate the ability of SOLAR to accurately estimate heritability and common environmental effects on simulated imaging phenotypes in various family structures. We found that heritability is reliably estimated with small family-based studies of 40 to 80 individuals, though subtle differences remain between the family structures. In an imaging application analysis, we found that with 80 subjects in any of the family structures, estimated heritability of white matter fractional anisotropy was biased by <10% for every region of interest. Results from these studies can be used when investigators are evaluating power in planning genetic analyzes.

Kuo, Hsien-Chi; Giger, Maryellen L.; Reiser, Ingrid; Drukker, Karen; Boone, John M.; Lindfors, Karen K.; Yang, Kai; Edwards, Alexandra; Sennett, Charlene A.

2014 Journal of Medical Imaging

doi: 10.1117/1.JMI.1.1.014501pmid: 32855995

Abstract.We present and evaluate a method for the three-dimensional (3-D) segmentation of breast masses on dedicated breast computed tomography (bCT) and automated 3-D breast ultrasound images. The segmentation method, refined from our previous segmentation method for masses on contrast-enhanced bCT, includes two steps: (1) initial contour estimation and (2) active contour-based segmentation to further evolve and refine the initial contour by adding a local energy term to the level-set equation. Segmentation performance was assessed in terms of Dice coefficients (DICE) for 129 lesions on noncontrast bCT, 38 lesions on contrast-enhanced bCT, and 98 lesions on 3-D breast ultrasound (US) images. For bCT, DICE values of 0.82 and 0.80 were obtained on contrast-enhanced and noncontrast images, respectively. The improvement in segmentation performance with respect to that of our previous method was statistically significant (p=0.002). Moreover, segmentation appeared robust with respect to the presence of glandular tissue. For 3-D breast US, the DICE value was 0.71. Hence, our method obtained promising results for both 3-D imaging modalities, laying a solid foundation for further quantitative image analysis and potential future expansion to other 3-D imaging modalities.

Srivastava, Ruchir; Gao, Xinting; Yin, Fengshou; Wong, Damon W. K.; Liu, Jiang; Cheung, Carol Y.; Wong, Tien Yin

2014 Journal of Medical Imaging

doi: 10.1117/1.JMI.1.1.014502pmid: 26158024

Abstract.This paper deals with automatic grading of nuclear cataract (NC) from slit-lamp images in order to reduce the efforts in traditional manual grading. Existing works on this topic have mostly used brightness and color of the eye lens for the task but not the visibility of lens parts. The main contribution of this paper is in utilizing the visibility cue by proposing gray level image gradient-based features for automatic grading of NC. Gradients are important for the task because in a healthy eye, clear visibility of lens parts leads to distinct edges in the lens region, but these edges fade as severity of cataract increases. Experiments performed on a large dataset of over 5000 slit-lamp images reveal that the proposed features perform better than the state-of-the-art features in terms of both speed and accuracy. Moreover, fusion of the proposed features with the prior ones gives results better than any of the two used alone.

Ossandon, Miguel R.; Phatak, Dhananjay S.; Sorg, Brian S.; Kalpakis, Konstantinos

2014 Journal of Medical Imaging

doi: 10.1117/1.JMI.1.1.014503pmid: 26158025

Abstract.Features of the tumor microenvironment (TME), such as hemoglobin saturation (HbSat), can provide valuable information on early development and progression of tumors. HbSat correlates with high metabolism and precedes the formation of angiogenic tumors; therefore, changes in HbSat profile can be used as a biomarker for early cancer detection. In this project, we develop a methodology to evaluate HbSat for forecasting early tumor development in a mouse model. We built a delta (δ) cumulative feature that includes spatial and temporal distribution of HbSat for classifying tumor/normal areas. Using a two-class (normal and tumor) logistic regression, the δ feature successfully forecasts tumor areas in two window chamber mice (AUC=0.90 and 0.85). To assess the performance of the logistic regression-based classifier utilizing the δ feature of each region, we conduct a 10-fold cross-validation analysis (AUC of the ROC=0.87). These results show that the TME features based on HbSat can be used to evaluate tumor progression and forecast new occurrences of tumor areas.

Wieringa, Fokko P.; Bouma, Henri; Eendebak, Pieter T.; van Basten, Jean-Paul A.; Beerlage, Harrie P.; Smits, Geert A. H. J.; Bos, Jelte E.

2014 Journal of Medical Imaging

doi: 10.1117/1.JMI.1.1.015001pmid: 26158026

Abstract.In comparison to open surgery, endoscopic surgery offers impaired depth perception and narrower field-of-view. To improve depth perception, the Da Vinci robot offers three-dimensional (3-D) video on the console for the surgeon but not for assistants, although both must collaborate. We improved the shared perception of the whole surgical team by connecting live 3-D monitors to all three available Da Vinci generations, probed user experience after two years by questionnaire, and compared time measurements of a predefined complex interaction task performed with a 3-D monitor versus two-dimensional. Additionally, we investigated whether the complex mental task of reconstructing a 3-D overview from an endoscopic video can be performed by a computer and shared among users. During the study, 925 robot-assisted laparoscopic procedures were performed in three hospitals, including prostatectomies, cystectomies, and nephrectomies. Thirty-one users participated in our questionnaire. Eighty-four percent preferred 3-D monitors and 100% reported spatial-perception improvement. All participating urologists indicated quicker performance of tasks requiring delicate collaboration (e.g., clip placement) when assistants used 3-D monitors. Eighteen users participated in a timing experiment during a delicate cooperation task in vitro. Teamwork was significantly (40%) faster with the 3-D monitor. Computer-generated 3-D reconstructions from recordings offered very wide interactive panoramas with educational value, although the present embodiment is vulnerable to movement artifacts.

Koch, Martin; Brost, Alexander; Bourier, Felix; Hornegger, Joachim; Strobel, Norbert

2014 Journal of Medical Imaging

doi: 10.1117/1.JMI.1.1.015002pmid: 26158027

Abstract.Catheter ablation is a common treatment option for drug-refractory atrial fibrillation. In many cases, pulmonary vein isolation is the treatment of choice. With current fluoro overlay methods or electroanatomic mapping systems, it is possible to visualize three-dimensional (3-D) anatomy as well as target ablation lines to provide additional context information. Today, however, these lines need to be set manually before the procedure by the physician, which may interrupt the clinical workflow. As a solution, we present an automatic approach for the planning of ablation target lines. Our method works on surface models extracted from 3-D images. To propose suitable ablation lines, a reference model annotated with reference ablation lines is nonrigidly registered to the model segmented from a new patient’s 3-D data. After registration, the reference plan is transferred from the reference anatomy to the individual patient anatomy. Due to the high anatomical variations observed in clinical practice, additional landmark constraints are employed in the registration process to increase the robustness of our approach. We evaluated our method on 43 clinical datasets by benchmarking it against professionally planned ablation lines and achieved an average error over all datasets of 2.7±2.0  mm. A qualitative evaluation of the ablation planning lines matched clinical expectations.