Surgical tools detection for intraoperative surgical navigation system is essential for better coordination among surgical team in operating room. Because Orthopaedic surgery (OS) differs from laparoscopic, due to a large variety of surgical instruments and techniques making its procedures complicated. Compared to usual object detection in natural images, OS video images are confounded by inhomogeneous illumination; it is hard to directly apply existing studies that are developed for others. Additionally, acquiring Orthopaedic surgery videos is difficult due to recording of surgery videos in restricted surgical environment. Therefore, we propose a deep learning (DL) approach for surgery tools detection in OS videos by integrating knowledge of diverse representative surgery and non-surgery images of tools into the model using transfer learning (TL) and data augmentation. The proposed method has been evaluated for five surgical tools using knee surgery images following 10-fold cross validation. It shows, proposed model (mAP 62.46%) outperforms over conventional model (mAP 60%).
Newborn brain MR image segmentation is a challenging problem because of variety of size, shape and MR signal
although it is the fundamental study for quantitative radiology in brain MR images. Because of the large difference
between the adult brain and the newborn brain, it is difficult to directly apply the conventional methods for the newborn brain. Inspired by the original fuzzy object model introduced by Udupa et al. at SPIE Medical Imaging 2011, called fuzzy shape object model (FSOM) here, this paper introduces fuzzy intensity object model (FIOM), and proposes a new image segmentation method which combines the FSOM and FIOM into fuzzy connected (FC) image segmentation. The fuzzy object models are built from training datasets in which the cerebral parenchyma is delineated by experts. After registering FSOM with the evaluating image, the proposed method roughly recognizes the cerebral parenchyma region based on a prior knowledge of location, shape, and the MR signal given by the registered FSOM and FIOM. Then, FC image segmentation delineates the cerebral parenchyma using the fuzzy object models. The proposed method has been evaluated using 9 newborn brain MR images using the leave-one-out strategy. The revised age was between -1 and 2 months. Quantitative evaluation using false positive volume fraction (FPVF) and false negative volume fraction (FNVF) has been conducted. Using the evaluation data, a FPVF of 0.75% and FNVF of 3.75% were achieved. More data collection and testing are underway.
KEYWORDS: Sensors, Biometrics, Data acquisition, Fuzzy logic, Electrodes, Feature extraction, Data processing, Gait analysis, Control systems, System identification
This paper describes a biometric personal authentication method based on fuzzy logic using dynamics of sole pressure
distribution while walking. The method employs a pair of right and left sole pressure data. These data are acquired by a
mat type load distribution sensor. The proposed method has two processes. First, we calculate a fuzzy degree of each
sole pressure data. In this process, we extract several gait features based on weight shift and shape of footprint. Fuzzy ifthen
rules for each registered person are introduced. In it, their parameters are statistically optimized in learning process.
Second, we combine fuzzy degrees of right and left sole. In this process, we employ five operators. The method
authenticates walking person with the combined fuzzy degree. We calculate the fuzzy degree of an interest person for all
registered persons, and identify the interest person as the registered person with the highest fuzzy degree. While, we
verify the interest person as the target person if the fuzzy degree of the interest person calculated for a target person is
higher than a threshold. In an experiment on 50 volunteers, we obtained low false rejection and false acceptance rates.
Home security in night is very important, and the system that watches a person's movements is useful in the security.
This paper describes a classification system of adult, child and the other object from distance distribution measured by an
infrared laser camera. This camera radiates near infrared waves and receives reflected ones. Then, it converts the time of
flight into distance distribution. Our method consists of 4 steps. First, we do background subtraction and noise rejection
in the distance distribution. Second, we do fuzzy clustering in the distance distribution, and form several clusters. Third,
we extract features such as the height, thickness, aspect ratio, area ratio of the cluster. Then, we make fuzzy if-then rules
from knowledge of adult, child and the other object so as to classify the cluster to one of adult, child and the other object.
Here, we made the fuzzy membership function with respect to each features. Finally, we classify the clusters to one with
the highest fuzzy degree among adult, child and the other object. In our experiment, we set up the camera in room and
tested three cases. The method successfully classified them in real time processing.
KEYWORDS: Sensors, Ultrasonics, Heart, Interference (communication), Signal detection, Fuzzy logic, Algorithm development, Signal to noise ratio, Detection and tracking algorithms, Data analysis
This paper discusses a data analysis by YURAGI for a heart rate non-constraining monitoring system Three signals are
employed: primary signal is obtained by a mat-type sensor, which is placed between a bed and subject, the second one is
obtained by an ultrasonic vibration senor attached to bed frame, and third one is Gaussian noise. We compare the results
from the synthesized data of the first and second signals with those of first signal and the noise. We employ weighted
sum as the synthesized method. We consider Gaussian noise as YURAGI. The extraction algorithm was developed based
on fuzzy logic. The comparison was done on 10 healthy volunteers and we evaluated the accuracy for various weight
ratio. Here, we must concern the accuracy because the tiny accuracy difference causes large difference in the autonomic
nerve system assessment. As the result, the results obtained from both synthesized signals were superior to that from
mat-type sensor signal only. Thus, YURAGI analysis is useful to for detecting heart rate by mat-type sensor.
KEYWORDS: Image segmentation, Liver, Magnetic resonance imaging, 3D image processing, Photovoltaics, Veins, 3D image reconstruction, Fuzzy logic, Medical imaging, Magnetism
This paper presents a fuzzy rule-based region growing method for segmenting two-dimensional (2-D) and three-dimensional (3- D) magnetic resonance (MR) images. The method is an extension of the conventional region growing method. The proposed method evaluates the growing criteria by using fuzzy inference techniques. The use of the fuzzy if-then rules is appropriate for describing the knowledge of the legions on the MR images. To evaluate the performance of the proposed method, it was applied to artificially generated images. In comparison with the conventional method, the proposed method shows high robustness for noisy images. The method then applied for segmenting the dynamic MR images of the liver. The dynamic MR imaging has been used for diagnosis of hepatocellular carcinoma (HCC), portal hypertension, and so on. Segmenting the liver, portal vein (PV), and inferior vena cava (IVC) can give useful description for the diagnosis, and is a basis work of a pres-surgery planning system and a virtual endoscope. To apply the proposed method, fuzzy if-then rules are derived from the time-density curve of ROIs. In the experimental results, the 2-D reconstructed and 3-D rendered images of the segmented liver, PV, and IVC are shown. The evaluation by a physician shows that the generated images are comparable to the hepatic anatomy, and they would be useful to understanding, diagnosis, and pre-surgery planning.
This paper shows a novel medical image segmentation method applied to blood vessel segmentation from magnetic resonance angiography volume data. The principle idea of the method is fuzzy information granulation concept. The method consists of 2 parts: (1) quantization and feature extraction, (2) iterative fuzzy synthesis. In the first part, volume quantization is performed with watershed segmentation technique. Each quantum is represented by three features, vascularity, narrowness and histogram consistency. Using these features, we estimate the fuzzy degrees of each quantum for knowledge models about MRA volume data. In the second part, the method increases the fuzzy degrees by selectively synthesizing neighboring quantums. As a result, we obtain some synthesized quantums. We regard them as fuzzy granules and classify them into blood vessel or fat by evaluating the fuzzy degrees. In the experimental result, three dimensional images are generated using target maximum intensity projection (MIP) and surface shaded display. The comparison with conventional MIP images shows that the unclarity region in conventional images are clearly depict in our images. The qualitative evaluation done by a physician shows that our method can extract blood vessel region and that the results are useful to diagnose the cerebral diseases.
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