Automated identification of single and clustered chromosomes for metaphase image analysis.

Background: Chromosome analysis is laborious and time-consuming. Automated methods can significantly increase the efficiency of chromosome analysis. For the automated analysis of chromosome images, single and clustered chromosomes must be identified. Herein, we propose a feature-based method for distinguishing between single chromosomes and clustered chromosome. Method: The proposed method comprises three main steps. In the first step, chromosome objects are segmented from metaphase chromosome images in advance. In the second step, seven features are extracted from each segmented object, i.e., the normalized area, area/boundary ratio, side branch index, exhaustive thresholding index, normalized minimum width, minimum concave angle, and maximum boundary shift. Finally, the segmented objects are classified as a single chromosome or chromosome cluster using a combination of the seven features. Results: In total, 43,391 segmented objects, including 39,892 single chromosomes and 3,499 chromosome clusters, are used to evaluate the proposed method. The results show that the proposed method achieves an accuracy of 98.92% by combining the seven features using support vector machine. Conclusions: The proposed method is highly effective in distinguishing between single and clustered chromosomes and can be used as a preprocessing procedure for automated chromosome image analysis.

A multi-stage fusion framework to classify breast lesions using deep learning and radiomics features computed from four-view mammograms.

BACKGROUND: Developing computer aided diagnosis (CAD) schemes of mammograms to classify between malignant and benign breast lesions has attracted a lot of research attention over the last several decades. However, unlike radiologists who make diagnostic decisions based on the fusion of image features extracted from multi-view mammograms, most CAD schemes are single-view-based schemes, which limit CAD performance and clinical utility. PURPOSE: This study aims to develop and test a novel CAD framework that optimally fuses information extracted from ipsilateral views of bilateral mammograms using both deep transfer learning (DTL) and radiomics feature extraction methods. METHODS: An image dataset containing 353 benign and 611 malignant cases is assembled. Each case contains four images: the craniocaudal (CC) and mediolateral oblique (MLO) view of the left and right breast. First, we extract four matching regions of interest (ROIs) from images that surround centers of two suspicious lesion regions seen in CC and MLO views, as well as matching ROIs in the contralateral breasts. Next, the handcrafted radiomics (HCRs) features and VGG16 model-generated automated features are extracted from each ROI resulting in eight feature vectors. Then, after reducing feature dimensionality and quantifying the bilateral and ipsilateral asymmetry of four ROIs to yield four new feature vectors, we test four fusion methods to build three support vector machine (SVM) classifiers by an optimal fusion of asymmetrical image features extracted from four view images. RESULTS: Using a 10-fold cross-validation method, results show that a SVM classifier trained using an optimal fusion of four view images yields the highest classification performance (AUC = 0.876 ± 0.031), which significantly outperforms SVM classifiers trained using one projection view alone, AUC = 0.817 ± 0.026 and 0.792 ± 0.026 for the CC and MLO view of bilateral mammograms, respectively (p < 0.001). CONCLUSIONS: The study demonstrates that the shift from single-view CAD to four-view CAD and the inclusion of both DTL and radiomics features significantly increases CAD performance in distinguishing between malignant and benign breast lesions.

Corneal-Smart Phone: A novel method to intelligently estimate postmortem interval.

The changes of postmortem corneal opacity are often used to roughly estimate the postmortem interval (PMI) in forensic practice. The difficulty associated with this time estimate is the lack of objective means to rapidly quantify postmortem corneal changes in crime scenes. This study constructed a data analysis model of PMI estimation and implemented an intelligent analysis system for examining the sequential changes of postmortem corneal digital images, named Corneal-Smart Phone, which can be used to quickly estimate PMI. The smart phone was used in combination with an attachment device that provided a darkroom environment and a steady light source to capture postmortem corneal images. By segmenting the corneal pupil region images, six color features, Red (R), Green (G), Blue (B), Hue (H), Saturation (S), Brightness (V) and four texture features Contrast (CON), Correlation (COR), Angular Second Moment (ASM), and Homogeneity (HOM), were extracted and correlated with PMI model. The results indicated that CON had the highest correlation with PMI (R2  = 0.983). No intra/intersubject variation in CON values were observed (p > 0.05). With the increase in ambient temperature or the decrease in humidity, the CON values were increased. PMI prediction error was <3 h within 36 h postmortem and extended to about 6-8 h after 36 h postmortem. The correct classification rate of the blind test samples was 82%. Our study provides a method that combines postmortem corneal image acquisition and digital image analysis to enable users to quickly obtain PMI estimation.

Applying a new quantitative image analysis scheme based on global mammographic features to assist diagnosis of breast cancer.

BACKGROUND AND OBJECTIVE: This study aims to develop and evaluate a unique global mammographic image feature analysis scheme to predict likelihood of a case depicting the detected suspicious breast mass being malignant for breast cancer. METHODS: From the entire breast area depicting on the mammograms, 59 features were initially computed to characterize the breast tissue properties at both spatial and frequency domain. Given that each case consists of two cranio-caudal and two medio-lateral oblique view images of left and right breasts, two feature pools were built, which contain the computed features from either two positive images of one breast or all the four images of two breasts. Next, for each feature pool, a particle swarm optimization (PSO) method was applied to determine the optimal feature cluster followed by training a support vector machine (SVM) classifier to generate a final score for predicting likelihood of the case being malignant. To test the scheme, we assembled a dataset involving 275 patients who had biopsy due to the suspicious findings on mammograms. Among them, 134 are malignant and 141 are benign. A ten-fold cross validation method was used to train and test the scheme. RESULTS: The classification performance levels measured by the areas under ROC curves are 0.79 ± 0.07 and 0.75 ± 0.08 when applying the SVM classifiers trained using image features computed from two-view and four-view images, respectively. CONCLUSIONS: This study demonstrates feasibility of developing a new global mammographic image feature analysis-based scheme to predict the likelihood of case being malignant without lesion segmentation.

Developing global image feature analysis models to predict cancer risk and prognosis.

In order to develop precision or personalized medicine, identifying new quantitative imaging markers and building machine learning models to predict cancer risk and prognosis has been attracting broad research interest recently. Most of these research approaches use the similar concepts of the conventional computer-aided detection schemes of medical images, which include steps in detecting and segmenting suspicious regions or tumors, followed by training machine learning models based on the fusion of multiple image features computed from the segmented regions or tumors. However, due to the heterogeneity and boundary fuzziness of the suspicious regions or tumors, segmenting subtle regions is often difficult and unreliable. Additionally, ignoring global and/or background parenchymal tissue characteristics may also be a limitation of the conventional approaches. In our recent studies, we investigated the feasibility of developing new computer-aided schemes implemented with the machine learning models that are trained by global image features to predict cancer risk and prognosis. We trained and tested several models using images obtained from full-field digital mammography, magnetic resonance imaging, and computed tomography of breast, lung, and ovarian cancers. Study results showed that many of these new models yielded higher performance than other approaches used in current clinical practice. Furthermore, the computed global image features also contain complementary information from the features computed from the segmented regions or tumors in predicting cancer prognosis. Therefore, the global image features can be used alone to develop new case-based prediction models or can be added to current tumor-based models to increase their discriminatory power.

Towards a modular decision support system for radiomics: A case study on rectal cancer.

Following the personalized medicine paradigm, there is a growing interest in medical agents capable of predicting the effect of therapies on patients, by exploiting the amount of data that is now available for each patient. In disciplines like oncology, where images and scans are available, the exploitation of medical images can provide an additional source of potentially useful information. The study and analysis of features extracted by medical images, exploited for predictive purposes, is termed radiomics. A number of tools are available for supporting some of the steps of the radiomics process, but there is a lack of approaches which are able to deal with all the steps of the process. In this paper, we introduce a medical agent-based decision support system capable of handling the whole radiomics process. The proposed system is tested on two independent data sets of patients treated for rectal cancer. Experimental results indicate that the system is able to generate highly performant centre-specific predictive model, and show the issues related to differences in data sets collected by different centres, and how such issues can affect the performance of the generated predictive models.

A new near-term breast cancer risk prediction scheme based on the quantitative analysis of ipsilateral view mammograms.

PURPOSE: To help improve efficacy of screening mammography and eventually establish an optimal personalized screening paradigm, this study aimed to develop and test a new near-term breast cancer risk prediction scheme based on the quantitative analysis of ipsilateral view of the negative screening mammograms. METHODS: The dataset includes digital mammograms acquired from 392 women with two sequential full-field digital mammography examinations. All the first ("prior") sets of mammograms were interpreted as negative during the original reading. In the sequential ("current") screening, 202 were proved positive and 190 remained negative/benign. For each pair of the "prior" ipsilateral mammograms, we adaptively fused the image features computed from two views. Using four different types of image features, we built four elastic net support vector machine (EnSVM) based classifiers. Then, the initial prediction scores form the 4 EnSVMs were combined to build a final artificial neural network (ANN) classifier that produces the final risk prediction score. The performance of the new scheme was evaluated by using a 10-fold cross-validation method and an assessment index of the area under the receiver operating characteristic curve (AUC). RESULTS: A total number of 466 features were initially extracted from each pair of ipsilateral mammograms. Among them, 51 were selected to build the EnSVM based prediction scheme. The AUC = 0.737 ±â€¯0.052 was yielded using the new scheme. Applying an optimal operating threshold, the prediction sensitivity was 60.4% (122 of 202) and the specificity was 79.0% (150 of 190). CONCLUSION: The study results showed moderately high positive association between computed risk scores using the "prior" negative mammograms and the actual outcome of the image-detectable breast cancers in the next subsequent screening examinations. The study also demonstrated that quantitative analysis of the ipsilateral views of the mammograms enabled to provide useful information in predicting near-term breast cancer risk.

Applying Quantitative CT Image Feature Analysis to Predict Response of Ovarian Cancer Patients to Chemotherapy.

RATIONALE AND OBJECTIVES: The study aimed to investigate the role of applying quantitative image features computed from computed tomography (CT) images for early prediction of tumor response to chemotherapy in the clinical trials for treating ovarian cancer patients. MATERIALS AND METHODS: A dataset involving 91 patients was retrospectively assembled. Each patient had two sets of pre- and post-therapy CT images. A computer-aided detection scheme was applied to segment metastatic tumors previously tracked by radiologists on CT images and computed image features. Two initial feature pools were built using image features computed from pre-therapy CT images only and image feature difference computed from both pre- and post-therapy images. A feature selection method was applied to select optimal features, and an equal-weighted fusion method was used to generate a new quantitative imaging marker from each pool to predict 6-month progression-free survival. The prediction accuracy between quantitative imaging markers and the Response Evaluation Criteria in Solid Tumors (RECIST) criteria was also compared. RESULTS: The highest areas under the receiver operating characteristic curve are 0.684 ± 0.056 and 0.771 ± 0.050 when using a single image feature computed from pre-therapy CT images and feature difference computed from pre- and post-therapy CT images, respectively. Using two corresponding fusion-based image markers, the areas under the receiver operating characteristic curve significantly increased to 0.810 ± 0.045 and 0.829 ± 0.043 (P < 0.05), respectively. Overall prediction accuracy levels are 71.4%, 80.2%, and 74.7% when using two imaging markers and RECIST, respectively. CONCLUSIONS: This study demonstrated the feasibility of predicting patients' response to chemotherapy using quantitative imaging markers computed from pre-therapy CT images. However, using image feature difference computed between pre- and post-therapy CT images yielded higher prediction accuracy.

Optimal reconstruction and quantitative image features for computer-aided diagnosis tools for breast CT.

PURPOSE: The purpose of this study is to determine the optimal representative reconstruction and quantitative image feature set for a computer-aided diagnosis (CADx) scheme for dedicated breast computer tomography (bCT). METHOD: We used 93 bCT scans that contain 102 breast lesions (62 malignant, 40 benign). Using an iterative image reconstruction (IIR) algorithm, we created 37 reconstructions with different image appearances for each case. In addition, we added a clinical reconstruction for comparison purposes. We used image sharpness, determined by the gradient of gray value in a parenchymal portion of the reconstructed breast, as a surrogate measure of the image qualities/appearances for the 38 reconstructions. After segmentation of the breast lesion, we extracted 23 quantitative image features. Using leave-one-out-cross-validation (LOOCV), we conducted the feature selection, classifier training, and testing. For this study, we used the linear discriminant analysis classifier. Then, we selected the representative reconstruction and feature set for the classifier with the best diagnostic performance among all reconstructions and feature sets. Then, we conducted an observer study with six radiologists using a subset of breast lesions (N = 50). Using 1000 bootstrap samples, we compared the diagnostic performance of the trained classifier to those of the radiologists. RESULT: The diagnostic performance of the trained classifier increased as the image sharpness of a given reconstruction increased. Among combinations of reconstructions and quantitative image feature sets, we selected one of the sharp reconstructions and three quantitative image feature sets with the first three highest diagnostic performances under LOOCV as the representative reconstruction and feature set for the classifier. The classifier on the representative reconstruction and feature set achieved better diagnostic performance with an area under the ROC curve (AUC) of 0.94 (95% CI = [0.81, 0.98]) than those of the radiologists, where their maximum AUC was 0.78 (95% CI = [0.63, 0.90]). Moreover, the partial AUC, at 90% sensitivity or higher, of the classifier (pAUC = 0.085 with 95% CI = [0.063, 0.094]) was statistically better (P-value < 0.0001) than those of the radiologists (maximum pAUC = 0.009 with 95% CI = [0.003, 0.024]). CONCLUSION: We found that image sharpness measure can be a good candidate to estimate the diagnostic performance of a given CADx algorithm. In addition, we found that there exists a reconstruction (i.e., sharp reconstruction) and a feature set that maximizes the diagnostic performance of a CADx algorithm. On this optimal representative reconstruction and feature set, the CADx algorithm outperformed radiologists.

Prediction of near-term breast cancer risk based on virtual optical density image / 中国医学影像技术

Objective To investigate the value of improving the prediction accuracy of near-term risk for developing breast cancer by transforming the original mammography image and fusing the different types of image features using the algorithm of machine learning.Methods The craniocaudal (CC) full-field digital mammography (FFDM) of 185 women were downloaded from the clinical database at the university of Pittsburgh medical center.Firstly,the original gray images were segmented and transformed into virtual optical density images.Then the asymmetry features were separately extracted from original gray images and virtual optical density images.Two decision tree classifiers of the first stage were trained based on the features extracted from two types of image.And the scores output from the two classifiers were used as input to train the second stage of one decision tree classifier.Leave-one-case-out method was used to validate the prediction performance of near-term risk of breast cancer.Results Using two-stage decision tree fusion method to predict breast cancer,the area under the ROC curve (AUC) was 0.9612±0.0132.And the sensitivity,specificity and prediction accuracy were 96.63％(86/89),91.67％(88/96) and 94.05％(174/185).Conclusion The features extracted from virtual optical density image have higher discriminatory power of predicting breast cancer.Fusing the two kinds of image features twice by two-stage decision tree method can help to improve the prediction accuracy of near-term risk of breast cancer.

Optic nerve head slope-based quantitative parameters for identifying open-angle glaucoma on SPECTRALIS OCT images.

PURPOSE: To investigate monitoring slope-based features of the optic nerve head (ONH) cup as open-angle glaucoma (OAG) occurs. METHOD: A dataset of 46 retrospective OCT cases was acquired from the SPECTRALIS Heidelberg Engineering OCT device. A set of five parameters, which are based on the ONH cup-incline, are measured on the OAG and normal subjects in the dataset. Then, three new ONH cup-shape indices were deduced. The ONH cup-incline parameters and ONH cup-shape indices are analyzed to estimate their clinical value. RESULTS: The statistical difference between measurements on normal and glaucoma eyes was remarkably significant for all of the analyzed parameters and indices (p value < 0.001). CONCLUSIONS: The geometric shape of the ONH cup can be transferred to numerical parameters and indices. The proposed ONH cup-incline parameters and ONH cup-shape indices have shown suggestive clinical value to identify the development of OAG. As OAG appears, the top ONH cup-incline parameters decrease while the bottom ONH cup-incline parameters increase. The ONH cup-shape indices suggest capability to discriminate OAG from normal eyes.

Sensitivity of Image Features to Noise in Conventional and Respiratory-Gated PET/CT Images of Lung Cancer: Uncorrelated Noise Effects.

The effect of noise on image features has yet to be studied in depth. Our objective was to explore how significantly image features are affected by the addition of uncorrelated noise to an image. The signal-to-noise ratio and noise power spectrum were calculated for a positron emission tomography/computed tomography scanner using a Ge-68 phantom. The conventional and respiratory-gated positron emission tomography/computed tomography images of 31 patients with lung cancer were retrospectively examined. Multiple sets of noise images were created for each original image by adding Gaussian noise of varying standard deviation equal to 2.5%, 4.0%, and 6.0% of the maximum intensity for positron emission tomography images and 10, 20, 50, 80, and 120 Hounsfield units for computed tomography images. Image features were extracted from all images, and percentage differences between the original image and the noise image feature values were calculated. These features were then categorized according to the noise sensitivity. The contour-dependent shape descriptors averaged below 4% difference in positron emission tomography and below 13% difference in computed tomography between noise and original images. Gray level size zone matrix features were the most sensitive to uncorrelated noise exhibiting average differences >200% for conventional and respiratory-gated images in computed tomography and 90% in positron emission tomography. Image feature differences increased as the noise level increased for shape, intensity, and gray-level co-occurrence matrix features in positron emission tomography and for gray-level co-occurrence matrix and gray-level size zone matrix features in conventional computed tomography. Investigators should be aware of the noise effects on image features.

Applying a computer-aided scheme to detect a new radiographic image marker for prediction of chemotherapy outcome.

BACKGROUND: To investigate the feasibility of automated segmentation of visceral and subcutaneous fat areas from computed tomography (CT) images of ovarian cancer patients and applying the computed adiposity-related image features to predict chemotherapy outcome. METHODS: A computerized image processing scheme was developed to segment visceral and subcutaneous fat areas, and compute adiposity-related image features. Then, logistic regression models were applied to analyze association between the scheme-generated assessment scores and progression-free survival (PFS) of patients using a leave-one-case-out cross-validation method and a dataset involving 32 patients. RESULTS: The correlation coefficients between automated and radiologist's manual segmentation of visceral and subcutaneous fat areas were 0.76 and 0.89, respectively. The scheme-generated prediction scores using adiposity-related radiographic image features significantly associated with patients' PFS (p < 0.01). CONCLUSION: Using a computerized scheme enables to more efficiently and robustly segment visceral and subcutaneous fat areas. The computed adiposity-related image features also have potential to improve accuracy in predicting chemotherapy outcome.

Quantitative measurement of adiposity using CT images to predict the benefit of bevacizumab-based chemotherapy in epithelial ovarian cancer patients.

The present study aims to quantitatively measure adiposity-related image features and to test the feasibility of applying multivariate statistical data analysis-based prediction models to generate a novel clinical marker and predict the benefit of epithelial ovarian cancer (EOC) patients with and without maintenance bevacizumab-based chemotherapy. A dataset involving computed tomography (CT) images acquired from 59 patients diagnosed with advanced EOC was retrospectively collected. Among them, 32 patients received maintenance bevacizumab following primary chemotherapy, while 27 did not. A computer-aided detection scheme was developed to automatically segment visceral and subcutaneous fat areas depicted on CT images of abdominal sections, and 7 adiposity-related image features were computed. Upon combining these features with the measured body mass index, multivariate data analyses were performed using three statistical models (multiple linear, logistic and Cox proportional hazards regressions) to analyze the association between the model-generated prediction results and the treatment outcome, including progression-free survival (PFS) and overall survival (OS) of the patients. The results demonstrated that applying all three prediction models yielded a significant association between the adiposity-related image features and patients' PFS or OS in the group of the patients who received maintenance bevacizumab (P<0.010), while there was no significant difference when these prediction models were applied to predict both PFS and OS in the group of patients that did not receive maintenance bevacizumab. Therefore, the present study demonstrated that the use of a quantitative adiposity-related image feature-based statistical model may generate a novel clinical marker to predict who will benefit among EOC patients receiving maintenance bevacizumab-based chemotherapy.

Applying a new quantitative global breast MRI feature analysis scheme to assess tumor response to chemotherapy.

PURPOSE: To develop a new quantitative global kinetic breast magnetic resonance imaging (MRI) features analysis scheme and assess its feasibility to assess tumor response to neoadjuvant chemotherapy. MATERIALS AND METHODS: A dataset involving breast MR images acquired from 151 cancer patients before neoadjuvant chemotherapy was used. Among them, 63 patients had complete response (CR) and 88 had partial response (PR) to chemotherapy based on the RECIST criterion. A computer-aided detection (CAD) scheme was applied to segment breast region depicted on the breast MR images and computed a total of 10 kinetic image features to represent parenchyma enhancement either from the entire two breasts or the bilateral asymmetry between the two breasts. To classify between CR and PR cases, we tested an attribution selected classifier that integrates with an artificial neural network and a Wrapper Subset Evaluator. The classifier was trained and tested using a leave-one-case-out (LOCO)-based cross-validation method. The area under a receiver operating characteristic curve (AUC) was computed to assess classifier performance. RESULTS: From the pool of initial 10 features, four features were selected by more than 90% times in the LOCO cross-validation iterations. Among them, three represent the bilateral asymmetry of kinetic features between two breasts. Using the classifier yielded AUC = 0.83 ± 0.04, which is significantly higher than using each individual feature to classify between CR and PR cases (P < 0.05). CONCLUSION: This study demonstrated that quantitative analysis of global kinetic features computed from breast MRI-acquired prechemotherapy has potential to generate a useful clinical marker that is associated with tumor response to neoadjuvant chemotherapy. J. Magn. Reson. Imaging 2016;44:1099-1106.

Early prediction of clinical benefit of treating ovarian cancer using quantitative CT image feature analysis.

BACKGROUND: In current clinical trials of treating ovarian cancer patients, how to accurately predict patients' response to the chemotherapy at an early stage remains an important and unsolved challenge. PURPOSE: To investigate feasibility of applying a new quantitative image analysis method for predicting early response of ovarian cancer patients to chemotherapy in clinical trials. MATERIAL AND METHODS: A dataset of 30 patients was retrospectively selected in this study, among which 12 were responders with 6-month progression-free survival (PFS) and 18 were non-responders. A computer-aided detection scheme was developed to segment tumors depicted on two sets of CT images acquired pre-treatment and 4-6 weeks post treatment. The scheme computed changes of three image features related to the tumor volume, density, and density variance. We analyzed performance of using each image feature and applying a decision tree to predict patients' 6-month PFS. The prediction accuracy of using quantitative image features was also compared with the clinical record based on the Response Evaluation Criteria in Solid Tumors (RECIST) guideline. RESULTS: The areas under receiver operating characteristic curve (AUC) were 0.773 ± 0.086, 0.680 ± 0.109, and 0.668 ± 0.101, when using each of three features, respectively. AUC value increased to 0.831 ± 0.078 when combining these features together. The decision-tree classifier achieved a higher predicting accuracy (76.7%) than using RECIST guideline (60.0%). CONCLUSION: This study demonstrated the potential of using a quantitative image feature analysis method to improve accuracy of predicting early response of ovarian cancer patients to the chemotherapy in clinical trials.

New techniques for assessing response after hypofractionated radiotherapy for lung cancer.

Hypofractionated radiotherapy (HFRT) is an effective and increasingly-used treatment for early stage non-small cell lung cancer (NSCLC). Stereotactic ablative radiotherapy (SABR) is a form of HFRT and delivers biologically effective doses (BEDs) in excess of 100 Gy10 in 3-8 fractions. Excellent long-term outcomes have been reported; however, response assessment following SABR is complicated as radiation induced lung injury can appear similar to a recurring tumor on CT. Current approaches to scoring treatment responses include Response Evaluation Criteria in Solid Tumors (RECIST) and positron emission tomography (PET), both of which appear to have a limited role in detecting recurrences following SABR. Novel approaches to assess response are required, but new techniques should be easily standardized across centers, cost effective, with sensitivity and specificity that improves on current CT and PET approaches. This review examines potential novel approaches, focusing on the emerging field of quantitative image feature analysis, to distinguish recurrence from fibrosis after SABR.