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In this article, a method to reduce artifacts on temporal difference images is introduced. The proposed method uses a nonrigid registration method for ground glass opacification (GGO), which is light in concentration and difficult to detect early. In this method, global matching, local matching, and three-dimensional (3D) elastic matching are performed on the current and previous images, and an initial temporal subtraction image is generated. After that, we use an Iris filter, which is the gradient vector concentration degree filter, to determine the initial GGO candidate regions and use supervoxel and graph cuts to segment region of interest in the 3D images. For each extracted region, a support vector machine is used to reduce the oversegmentation. The voxel matching is applied to generate the final temporal difference image, emphasizing the GGO regions while reducing the artifact.

While diagnosis imaging is an indispensable technology in medical field, it causes an increase in the burden to radiologists. In recent years, computer-aided diagnosis (CAD) system for supporting a radiologist has been developed to solve this problem. Temporal subtraction, which is one of CAD, is a technique to generate images emphasizing temporal changes in lesions and facilitates diagnosis of radiologists. To make a temporal subtraction image, image registration technique is required. In this chapter, we propose an image registration method for image registration of current image and previous image to generate temporal subtraction images in a short time. The proposed method consists of three steps: (i) segmentation of the region of interest (ROI) using position information of the spine based on anatomical information, (ii) using global image matching to select pairs of previous image and current image in which the same portion is depicted, and iii) final image matching based on salient region features (SRF). We performed our registration technique to the synthetic data and confirmed usefulness of the proposed method. The rotated synthesis image gives TP 100.0% and FP 12.16%. The synthesis image obtained by applying a Gaussian filter gives TP 70.40% and FP 0.00%. The synthesis image obtained by adding artificial pseudo-lesion region gives TP 99.45% and FP 17.89%. The synthesis image obtained by adding random noise of 5% gives TP 83.05% and FP 16.95%. Furthermore, radiologist conducted comparative experiments without and with temporal subtraction images created by proposed method. As a result, radiologist showed high reading performance by using temporal subtraction images.

Purpose To assess the effectiveness of a CT temporal subtraction (TS) method on radiologists’ performance in sclerotic metastasis detection in the thoracolumbar spine. Materials and methods 20 pairs (current and previous CTs) of standard-dose CT and their TS images in patients with sclerotic bone metastasis and 20 pairs (current and previous CTs) of those in patients without bone metastasis were used for an observer performance study. A total of 135 lesions were identified as the reference standard of actionable lesions (sclerotic metastasis newly appeared or increased in size or in attenuation). 4 attending radiologists and 4 radiology residents participated in this observer study. Ratings and locations of “lesions” determined by the observers were utilized for assessing the statistical significance of differences between radiologists' performances without and with the CT-TS images in JAFROC analysis. The statistical significance of differences in the reviewing time was determined by a two-tailed paired t-test. Results The average figure-of-merit (FOM) values for all but one radiologist increased to a statistically significant degree, from 0.856 without the CT-TS images to 0.884 with the images (P = .037). The average sensitivity for detecting the actionable lesions was improved from 60.7 % to 72.5% at a false-positive rate of 0.15 per case by use of the CT-TS images. The average reading time with CT-TS images was significantly shorter than that without (150.6 s vs. 166.5 s, P = .004). Conclusion The use of CT-TS would improve the observer performance for the detection of the sclerotic bone metastasis in the thoracolumbar spine.

Purpose The objective of this study is to determine the quality of chest X-ray images using a deep convolutional neural network (DCNN) and a rule base without performing any visual assessment. A method is proposed for determining the minimum diagnosable exposure index (EI) and the target exposure index (EIt). Methods The proposed method involves transfer learning to assess the lung fields, mediastinum, and spine using GoogLeNet, which is a type of DCNN that has been trained using conventional images. Three detectors were created, and the image quality of local regions was rated. Subsequently, the results were used to determine the overall quality of chest X-ray images using a rule-based technique that was in turn based on expert assessment. The minimum EI required for diagnosis was calculated based on the distribution of the EI values, which were classified as either suitable or non-suitable and then used to ascertain the EIt. Results The accuracy rate using the DCNN and the rule base was 81%. The minimum EI required for diagnosis was 230, and the EIt was 288. Conclusion The results indicated that the proposed method using the DCNN and the rule base could discriminate different image qualities without any visual assessment; moreover, it could determine both the minimum EI required for diagnosis and the EIt.

Bone diseases include rheumatoid arthritis and osteoporosis. Although visual screening using computed radiography (CR) images is an effective method for diagnosing osteoporosis, there are some similar diseases that exhibit low bone mass status. To this end, we aim to develop a computer-aided diagnostic (CAD) system to support the automatic diagnosis of osteoporosis from CR images. In this paper, we use convolutional neural network (CNN) and multiscale gradient vector flow snakes (MSGVF Snakes) algorithms to segment each finger bone regions from the CR image. The proposed method is applied to 15 cases, 92.95 [%] of the true positive rates, 2.21 [%] of the false positive rates, 7.05 [%] of the false negative rates are obtained respectively.

Segmentation of the bone metastasis area in medical images can reduce the workload for diagnosis and treatment. However, there are various shapes and sizes of bone metastasis also affected by noise. As a result, it is difficult to segment using classical segmentation methods. In this paper, we propose a convolutional neural network model-based segmentation method. The proposed method easily predicts the contour and location of the lesion area using side connection and modified network. In this study, we modified again the modified HED network to match the characteristics of bone metastasis. The experimental results using the proposed method for segmenting bone metastasis in the lesion area has 79.8[%] of TP rate and 69.2[%] of IOU rate.

In recent years, the number of death due to lung cancer is increasing year by year worldwide. Early detection and early treatment of lung cancer are important. Especially, early detection of the abnormalities on thoracic MDCT images detection of small nodules is required in visual screening. Although a CT apparatus is used for the examination, the burden on the image interpretation doctor is large due to the high performance of the CT, so the diagnostic accuracy may be reduced. In this paper, we propose an image analysis method to detect abnormal shadows from chest CT images automatically. The initial lesion candidate areas are extracted by using temporal subtraction technique that emphasizes temporal change by subtracting from a current image to previous one which is obtained same subject. The image of the area is given as input and classification is performed by CNN (Convolutional Neural Network). In the discrimination experiment based on our proposed method, 90.26 [%] of true positive rates and 13.58 [%] of false positive rates are obtained from the 49 clinical cases.

We aimed to assess the additive value of the split-bolus single-phase computed tomography (CT) scan protocol to preoperatively assess patients with lung cancer, who were referred for video-assisted thoracic surgery, when compared to a standard staging CT protocol. We included 160 patients with lung cancer who underwent a split-bolus single-phase CT scan protocol (split-bolus protocol), which can acquire whole-body staging CT and pulmonary artery-vein separation CT angiography (PA–PV CTA) in a single acquisition and 160 patients who underwent whole-body staging CT (standard protocol). We compared the quality of the staging CT images of hepatic parenchyma, portal vein, and hepatic vein between both protocols. We also investigated image quality on PA–PV CTA images in the split-bolus protocol and recorded the number of patients that underwent the 3D PA–PV CTA imaging process. The split-bolus protocol for staging CT images demonstrated a slightly higher enhancement with regard to the hepatic parenchyma (p = 0.007) and hepatic vein (p = 0.006) than the standard protocol. There was no significant difference in the quality of the staging CT images between both protocols (p = 0.067). The mean CT number for the main pulmonary artery and the left atrium for the PA–PV CTA images in the split-bolus protocol were 289.1 HU and 172.8 HU, respectively. Among the images associated with the split-bolus protocol, 98.1% were of appropriate quality for 3D PA–PV CTA imaging. The split-bolus protocol is a dose-efficient protocol to acquire the staging CT and PA–PV CTA images in a single session and provides sufficient image quality for preoperative assessment in patients with lung cancer.

Purpose The absorbed dose at the image receptor in digital X-ray systems increases with an incorrect adjustment of the X-ray tube current-time product (mAs). Accordingly, the exposure index, target exposure index, and deviation index (DI) are proposed as absorbed dose optimization tools. We aimed at reducing the variation of DI in a short period by employing the mAs value determined by previously used mAs and DI. Methods We developed software that automatically calculates mAs for subsequent X-ray examinations based on mAs and DI values from prior examinations. Portable chest X-ray examinations in an intensive care unit (ICU) were performed for 16 weeks. The software was not used for the first 10 weeks in 406 cases and was used for the remaining 6 weeks in 216 cases. The changes in the non-conformance rate of DI for 16 weeks were evaluated using the p-chart used for quality control. The effect of the software on image noise was also evaluated. Results In total, 42% of cases had a DI range of −1 to 1 without using the software; this increased to 81% when using the software. Averages and variances of DI in cases with and without the software demonstrated statistically significant differences. From the p-chart, the non-conformance rate of DI was shown to decrease when using software. The software also worked for reducing the variation in image noise. Conclusions Our method reduced the variation in DI in a short period of time.

AutomaticsegmentationofliverregionsfromabdominalMRimagesisoneofthechallengesincom- puter aided diagnosis (CAD) system. Probabilistic Atlas (PA) based methods are considered e ffi cient and robust in liver segmentation because the knowledge of distribution of object is incorporated in segmenting procedure. However, the estimation of the parameters of PA impacts the precise of segment results directly. In this paper, we proposed a novel method using region growing method with PA. We carried out experiments by the proposed method on 7 cases of abdominal contrast-enhanced magnetic resonance (MR) Images. The experimental results show that the performance of the method using region growing with probabilistic atlas is higher than that without probabilistic atlas.

OBJECTIVE: To assess the clinical performance of three-dimensional stereoscopic digital mammography (3DsDM) compared with two-dimensional digital mammography (2DDM) for breast lesion diagnosis with jackknife free-response receiver operating characteristics (JAFROC) observer study. METHODS: 40 pairs of standard-dose 2DDM and their 3DsDM images were used for an observer performance study. A total of 18 lesions were identified as the reference standard of actionable breast lesions (Breast Imaging Reporting and Data System Category 3 or more) by two breast radiologists. Ratings and locations of “lesions” determined by observers were utilized for assessing the statistical significance of differences between eight radiologists’ performances with the 2DDM images and with the 3DsDM images in jackknife free-response receiver operating characteristic analysis. RESULTS: The average figure-of-merit values for all radiologists increased to a statistically significant degree, from 0.859 with the 2DDM images to 0.936 with the 3DsDM images (p < 0.001). The average sensitivity for detecting actionable lesions was improved from 74.3 to 92.4% at a false-positive rate of 0.2 per case by use of the 3DsDM images. The mean reading time per case with 2DDM images was not significantly different from that with 3DsDM images. CONCLUSION: The use of 3DsDM would improve the observer performance for breast lesion without considerably extending the reading time. ADVANCES IN KNOWLEDGE: Use of 3DsDM improves radiologists’ performance for breast lesion detection.

Osteoporosis is known as a disease of bone. Visual screening using Computed Radiography (CR) images is an effective method for osteoporosis, however, there are many similar diseases that exhibit state of low bone mass. In this paper, we propose an automatic identification method of osteoporosis from phalanges CR images. In the proposed method, we implement a classifier based on Deep Convolutional Neural Network (DCNN), and identify unknown CR images as normal or abnormal. For training and evaluating of CNN, we use pseudo color images. In the experiment, we apply our proposal method to 101 cases and TPR of 64.7 [%] and FPR of 6.51 [%] were obtained.

In recent years, the death rate caused by lung cancer is increasing. To detect the lung cancer, multi detector-row computed tomography (MDCT) images are used in visual screening. Lung cancer can be easily detected by using the chest MDCT images, however, it has enormous images and burden to radiologists. Research and development of the computer aided diagnosis (CAD) system have been assisted the diagnosis. As one of the CAD technologies, temporal subtraction technique is possible to emphasize the changing interval on the CT images. It uses subtraction operation between previous and current CT images of the same patient. On the other hand, pattern recognition using image reconstruction by sparse coding method has attracted attention. This technique is mathematically modeling the information processing by the primary visual cortex of human. It is the technique for representing images by the linear combination of a small number of basis. In this paper, candidate nodules under 20[mm] were segmented from temporal subtraction images based on the 3D sparse coding technique.3D sparse coding is three dimensional expansion of the sparse coding. Also, we classified the final candidate nodules using support vector machine (SVM) method based on coefficient matrix which are obtained by the 3D sparse coding. We applied proposed method to 31 cases of chest MDCT images in which the number of nodules was more than one. We achieved experimental result with true positive rates (TPR) of 70.2[[%], and false positive rates (FP) of 34.7[/scan], respectively.

Cancer is a leading cause of death both in Japan and worldwide. Detection of cancer region in CT images is the most important task to early detection. Recently, visual screening based on CT images become useful tools for cancer detection. However, due to the large number of images and the complexity of the image processing algorithms, image processing technique is still required a high screening quality. To overcome this problem, some computer aided diagnosis (CAD) algorithms are proposed. In this paper, we have designed and developed a framework combining machine learning based on deep convolutional neural networks (DCNN) and temporal subtraction techniques based on non-rigid image registration algorithm. Our main classification method can be built into three main steps; i) pre-processing for image segmentation, ii) image matching for registration, and iii) classification of abnormal regions based on machine learning algorithms. We performed our proposed technique to 25 thoracic MDCT sets and obtained true positive rates of 92.31 [%], false positive rates of 6.32 [/case] were obtained.

To evaluate the usefulness of the CT temporal subtraction (TS) method for the detection of the lung cancer with predominant ground-glass opacity (LC-pGGO). Twenty-five pairs of CT and their TS images in patients with LC-pGGO (31 lesions) and 25 pairs of those in patients without nodules were used for an observer performance study. Eight radiologists participated and the statistical significance of differences with and without the CT-TS was assessed by JAFROC analysis. The average figure-of-merit (FOM) values for all radiologists increased to a statistically significant degree, from 0.861 without CT-TS to 0.912 with CT-TS (p < .001). The average sensitivity for detecting the actionable lesions improved from 73.4 % to 85.9 % using CT-TS. The reading time with CT-TS was not significantly different from that without. The use of CT-TS improves the observer performance for the detection of LC-pGGO. • CT temporal subtraction can improve the detection accuracy of lung cancer. • Reading time with temporal subtraction is not different from that without. • CT temporal subtraction improves observer performance for ground-glass/subsolid nodule detection.

A 61-year-old woman presented with the chief complaint of melena. She was diagnosed with rectal cancer via colonoscopy. Computed tomography (CT) revealed a rectal cancer with wall thickening, accompanied by several regional lymph node metastases with no distant metastasis. The tumor stage was cT3, cN2a, cM0 according to the TNM Classification of Malignant Tumors (7th Edition, UICC). Preoperative chemoradiotherapy (CRT) (UFT 400 mg/day tegafur-uracil and 75 mg/day Leucovorin; 1.8 Gy in 25 fractions, total 45 Gy) was administered. Eight weeks after CRT, laparoscopy-assisted low anterior resection was performed. A pathological examination revealed that both the primary site and regional lymph nodes had no residual cancer cells, and a diagnosis of pathological complete response was made. The patient has been disease-free for 4 years since the operation. We report a case of rectal cancer that was successfully treated via preoperative CRT. This case may aid the development of a standard therapy for advanced rectal cancer.

Recently, CT (Computed Tomography) scanner is used for detecting the abnormalities because CT scanner gradually becomes high resolution and high speed. However, with the improvement of the resolution of CT images, the number of CT images becomes huge. Therefore, radiologists have to analyze huge number of images and they sometimes misdiagnoses are happened. Hence, to deal with this problem the CAD (Computer Aided Diagnosis) system is developed. One of the CAD systems called temporal subtraction technique is useful to detect abnormalities in medical field. There is no viewer system which displays abnormal region using temporal subtraction technique. In this paper, we develop a novel temporal subtraction technique to help the radiologists to reduce diagnostic ti me and improve the diagnostic accuracy.

In recent years, medical imaging is important in medical diagnosis for early detection of lesions. However, a large number of images increases the stress to the radiologist. Therefore, CAD (Computer Aided Diagnosis) system is expected to reduce such burden.

The exposure index (EI) was proposed as a new X-ray dose index by the International Electrotechnical Commission (IEC) and has since been implemented as an international standard. The EI is calculated by use of an approximation equation under IEC-specified calibration conditions. However, several factors encountered in clinical practice, including the patient’s body thickness and the tube voltage, differ with regard to these calibration conditions. We, therefore, require a solid water phantom-based function that can incorporate the IEC-specified conditions and different subject thicknesses to evaluate the effects of subject thickness on the EI. Here, we assumed average thicknesses of 10 cm for a child, 15 cm for slender patients, and 21 cm for an average adult abdomen and we evaluated errors, that are included in the EI, which were calculated by use of the function. Our results suggested that the EI depends on the subject thickness. At the 21-cm thickness (average adult abdomen), the display EI exhibited a small error level. In contrast, EI values calculated from the calibration conditions exhibited maximum errors that were as high as 34 % at the lower subject thicknesses (10 and 15 cm), suggesting a significant influence of the subject thickness on the EI. In conclusion, the EI should be used cautiously during the examination of children and thin patients, with a complete understanding of the discrepancy revealed by our study results.

As two common orthopedic diseases, there are rheumatoid arthritis (RA) and osteoporosis. And early detection and early treatment are important for these diseases. However, some problem such as mass screening on data sets, mis-diagnosis are still remained in visual screening. In order to solve these problems and reduce the burden to physicians, needs of an automatic diagnosis system capable of performing quantitative analysis is anticipated. In this paper, we carry out the development of a registration method of phalanges regions from CR images of the hand to detect temporal changes of phalanges regions by alignment between previous image and current one. The proposed method is carried out registration of phalanges regions using salient region features (SRF) in image. Specifically, SRF are detected from phalanges regions. Then, rigid registration technique is performed using deformation amount based on SRF. We applied our method on 84 pairs of CR temporal images of phalanges regions.