IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
Image segmentation is still an active reason of research, a relevant research area
in computer vision and hundreds of image segmentation techniques have been proposed by
the researchers. All proposed techniques have their own usability and accuracy. In this paper
we are going present a review of some best lung nodule existing detection and segmentation
techniques. Finally, we conclude by focusing one of the best methods that may have high
level accuracy and can be used in detection of lung very small nodules accurately.
Lung Cancer Detection on CT Images by using Image Processingijtsrd
This project is mainly based on image processing technique. In this work MATLAB have been used through every procedure made. Image processing techniques are widely use in bio-medical sector. The objective of our work is noise removal operation, thresholding, gray scale imaging, histogram equalization, texture segmentation, and morphological operation. Detection of lung cancer from computed tomography (CT) images is done by using MATLAB software. By using these methods the work has been done on CT images and the final tumor area has been shown with pixel values. Bindiya Patel | Dr. Pankaj Kumar Mishra | Prof. Amit Kolhe"Lung Cancer Detection on CT Images by using Image Processing" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-3 , April 2018, URL: http://www.ijtsrd.com/papers/ijtsrd11674.pdf http://www.ijtsrd.com/engineering/electronics-and-communication-engineering/11674/lung-cancer-detection-on-ct-images-by-using-image-processing/bindiya-patel
This work was presented at the first Annual IEEE Topical Conference on Biomedical Wireless Technologies, Networks, and Sensing Systems (BioWireleSS) held as part of the IEEE Radio and Wireless Symposium 2011, in Phoenix, AZ.
This paper explains new imaging techniques that show promising results in breast cancer detection. The
presented techniques use microwave-based methods, wavelet analyses, and neural networks to get a
suitable resolution for the breast image. One of the presented techniques (hybrid method) uses a
combination of microwaves and acoustic signals to improve the detection capability. Some promising
results are shown and explained.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
Image segmentation is still an active reason of research, a relevant research area
in computer vision and hundreds of image segmentation techniques have been proposed by
the researchers. All proposed techniques have their own usability and accuracy. In this paper
we are going present a review of some best lung nodule existing detection and segmentation
techniques. Finally, we conclude by focusing one of the best methods that may have high
level accuracy and can be used in detection of lung very small nodules accurately.
Lung Cancer Detection on CT Images by using Image Processingijtsrd
This project is mainly based on image processing technique. In this work MATLAB have been used through every procedure made. Image processing techniques are widely use in bio-medical sector. The objective of our work is noise removal operation, thresholding, gray scale imaging, histogram equalization, texture segmentation, and morphological operation. Detection of lung cancer from computed tomography (CT) images is done by using MATLAB software. By using these methods the work has been done on CT images and the final tumor area has been shown with pixel values. Bindiya Patel | Dr. Pankaj Kumar Mishra | Prof. Amit Kolhe"Lung Cancer Detection on CT Images by using Image Processing" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-3 , April 2018, URL: http://www.ijtsrd.com/papers/ijtsrd11674.pdf http://www.ijtsrd.com/engineering/electronics-and-communication-engineering/11674/lung-cancer-detection-on-ct-images-by-using-image-processing/bindiya-patel
This work was presented at the first Annual IEEE Topical Conference on Biomedical Wireless Technologies, Networks, and Sensing Systems (BioWireleSS) held as part of the IEEE Radio and Wireless Symposium 2011, in Phoenix, AZ.
This paper explains new imaging techniques that show promising results in breast cancer detection. The
presented techniques use microwave-based methods, wavelet analyses, and neural networks to get a
suitable resolution for the breast image. One of the presented techniques (hybrid method) uses a
combination of microwaves and acoustic signals to improve the detection capability. Some promising
results are shown and explained.
Prediction of lung cancer is most challenging problem due to structure of cancer cell, where most of the cells are overlapped each other. The image processing techniques are mostly used for prediction of lung cancer and also for early detection and treatment to prevent the lung cancer. To predict the lung cancer various features are extracted from the images therefore, pattern recognition based approaches are useful to predict the lung cancer. Here, a comprehensive review for the prediction of lung cancer by previous researcher using image processing techniques is presented. The summary for the prediction of lung cancer by previous researcher using image processing techniques is also presented.
Early detection of cancer is the most promising way to enhance a patient's chance for survival. This paper presents a computer-aided classification method using computed tomography (CT) images of the lung based on ensemble of three classifiers including MLP, KNN and SVM. In this study, the entire lung is first segmented from the CT images and specific features like Roundness, Circularity, Compactness, Ellipticity, and Eccentricity are calculated from the segmented images. These morphological features are used for classification process in a way that each classifier makes its own decision. Finally, majority voting method is used to combine decisions of this ensemble system. The performance of this system is evaluated using 60 CT scans collected by Lung Image Database Consortium (LIDC) and the results show good improvement in diagnosing of pulmonary nodules.
Research into the effectiveness of daily image guided radiotherapy on the pro...Genesis Care
To assess the effect of frequency of verification imaging on the dose delivered to target volume and organs at risk, during a course of image-guided radiotherapy (IGRT) for prostate cancer.
Digital Breast Tomosynthesis with Minimal CompressionDavid Scaduto
Breast compression is utilized in mammography to improve image quality and reduce radiation dose. Lesion conspicuity is improved by reducing scatter effects on contrast and by reducing the superposition of tissue structures. However, patient discomfort due to breast compression has been cited as a potential cause of noncompliance with recommended screening practices. Further, compression may also occlude blood flow in the breast, complicating imaging with intravenous contrast agents and preventing accurate quantification of contrast enhancement and kinetics. Previous studies have investigated reducing breast compression in planar mammography and digital breast tomosynthesis (DBT), though this typically comes at the expense of degradation in image quality or increase in mean glandular dose (MGD). We propose to optimize the image acquisition technique for reduced compression in DBT without compromising image quality or increasing MGD. A zero-frequency signal-difference-to-noise ratio model is employed to investigate the relationship between tube potential, SDNR and MGD. Phantom and patient images are acquired on a prototype DBT system using the optimized imaging parameters and are assessed for image quality and lesion conspicuity. A preliminary assessment of patient motion during DBT with minimal compression is presented.
Prediction of lung cancer is most challenging problem due to structure of cancer cell, where most of the cells are overlapped each other. The image processing techniques are mostly used for prediction of lung cancer and also for early detection and treatment to prevent the lung cancer. To predict the lung cancer various features are extracted from the images therefore, pattern recognition based approaches are useful to predict the lung cancer. Here, a comprehensive review for the prediction of lung cancer by previous researcher using image processing techniques is presented. The summary for the prediction of lung cancer by previous researcher using image processing techniques is also presented.
Early detection of cancer is the most promising way to enhance a patient's chance for survival. This paper presents a computer-aided classification method using computed tomography (CT) images of the lung based on ensemble of three classifiers including MLP, KNN and SVM. In this study, the entire lung is first segmented from the CT images and specific features like Roundness, Circularity, Compactness, Ellipticity, and Eccentricity are calculated from the segmented images. These morphological features are used for classification process in a way that each classifier makes its own decision. Finally, majority voting method is used to combine decisions of this ensemble system. The performance of this system is evaluated using 60 CT scans collected by Lung Image Database Consortium (LIDC) and the results show good improvement in diagnosing of pulmonary nodules.
Research into the effectiveness of daily image guided radiotherapy on the pro...Genesis Care
To assess the effect of frequency of verification imaging on the dose delivered to target volume and organs at risk, during a course of image-guided radiotherapy (IGRT) for prostate cancer.
Digital Breast Tomosynthesis with Minimal CompressionDavid Scaduto
Breast compression is utilized in mammography to improve image quality and reduce radiation dose. Lesion conspicuity is improved by reducing scatter effects on contrast and by reducing the superposition of tissue structures. However, patient discomfort due to breast compression has been cited as a potential cause of noncompliance with recommended screening practices. Further, compression may also occlude blood flow in the breast, complicating imaging with intravenous contrast agents and preventing accurate quantification of contrast enhancement and kinetics. Previous studies have investigated reducing breast compression in planar mammography and digital breast tomosynthesis (DBT), though this typically comes at the expense of degradation in image quality or increase in mean glandular dose (MGD). We propose to optimize the image acquisition technique for reduced compression in DBT without compromising image quality or increasing MGD. A zero-frequency signal-difference-to-noise ratio model is employed to investigate the relationship between tube potential, SDNR and MGD. Phantom and patient images are acquired on a prototype DBT system using the optimized imaging parameters and are assessed for image quality and lesion conspicuity. A preliminary assessment of patient motion during DBT with minimal compression is presented.
Micro-optics is an indispensable key enabling technology (KET) for many applications today. The important role of micro-optical components is based on three different motivations: miniaturization, high functionality and packaging aspects. It is obvious that miniaturized systems require micro-optics for light focusing, light shaping and imaging. More important for industrial applications is the high functionality of micro-optics that allows combining these different functions in one element. In DUV Lithography Steppers and Scanners an extremely precise beam shaping of the Excimer laser profile is required. High-precision diffractive optical elements are well suited for this task. For Wafer-Level Cameras (WLC) and fiber optical systems the packaging aspects are more important. Wafer-Level Micro-Optics technology allows manufacturing and packaging some thousands of sub-components in parallel. We report on the state of the art in wafer-based manufacturing, testing and packaging.
Keywords: Micro-optics, microlens array, diffractive optical elements, wafer-level optics, wafer-level packaging, beam shaping, fiber coupling, array illumination, Shack-Hartmann, confocal microscope, slow-axis collimator.
Wireless Sensing by Passive Radiofrequency Identification: Research, Systems ...RADIO6ENSE Srl
RADIO6ENSE is a spin-off of the University of Roma "Tor Vergata", aiming at developing short-range distributed RFID sensing technology to discreetly measure and quantify our interaction with things and with the surrounding spaces.
RADIO6ENSE designs and develops RFID Sensing and Identification Platforms for Industrial, Civil, Aerospace and Biomedical applications for Smart Cities and the emerging Internet of Things.
Mainly supported today by flip-chip wafer bumping, 3D WLP, and WLCSP; the long term growth of the equipment and materials business will be supported by the expansion of 3D TSV stack platforms.
TSV integration is creating growth and significant interest in the equipment & materials industry
Mainly supported today by flip-chip wafer bumping, the equipment market generated revenue of more than $930M in 2013. It is expected that this equipment market revenue will peak at almost $2.5B. It is fueled by the 3D IC technology with TSV interconnects, an area offering opportunities for new developments in equipment modification—equipment that is much more expensive than the tools used for established Advanced Packaging platforms (3D WLP, WLCSP, flip-chip wafer bumping). Indeed, 2015 will be the key turning point for the adoption of 3D TSV Stacks since the memory manufacturers, such as Samsung, SK Hynix, Micron, have already started to ship prototypes this year and might be ready to enter in high-volume manufacturing next year....
More information on that report at: http://www.i-micronews.com/advanced-packaging-report/product/equipment-materials-for-3dic-wafer-level-packaging-applications.html#description
Humidity Sensors from the main players analyzed and compared!
Humidity sensors are integrated in many different consumer applications; in each of them different specifications are required. Moreover humidity sensors are integrated in environmental sensors for mobile applications; especially in harsh environment devices. An increase of humidity sensors market is forecasted by next years.
In this report, humidity sensors from different suppliers and different generations are compared in term of technology choice, manufacturing process and cost.
Single-chip and multi-chip devices are compared between suppliers and references evolution.
Size and technology is very different for every player. Nevertheless, the mainly used technology is the capacitive one. Moreover the majority of components are single-chip devices. Some devices, such as Bosch environmental sensor for example, are paving the way for the multichip integration.
The report includes a description of each humidity sensor device and a comprehensive supply chain evaluation. The cost of each device is estimated and compared.
Arduino and sensors for water level, soil moisture, temperature & relative humidity for application in the ClimaAdapt Project areas - Nagarjuna Sagar Project Left and Right Canals in the States of Telangana and Andhra Pradesh for water use efficiency - Canal and On Farm
A Review of Super Resolution and Tumor Detection Techniques in Medical Imagingijtsrd
Images with high resolution are desirable in many applications such as medical imaging, video surveillance, astronomy etc. In medical imaging, images are obtained for medical investigative purposes and for providing information about the anatomy, the physiologic and metabolic activities of the volume below the skin. Medical imaging is an important diagnosis instrument to determine the presence of certain diseases. Therefore increasing the image resolution should significantly improve the diagnosis ability for corrective treatment. Brain tumor detection is used for identifying the tumor present in the Brain. MRI images help the doctors for identifying the Brain tumor size and shape of the tumor. The purpose of this report to provide a survey of research related super resolution and tumor detection methods. Fathimath Safana C. K | Sherin Mary Kuriakose ""A Review of Super Resolution and Tumor Detection Techniques in Medical Imaging"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-3 , April 2019, URL: https://www.ijtsrd.com/papers/ijtsrd23525.pdf
Paper URL: https://www.ijtsrd.com/engineering/electronics-and-communication-engineering/23525/a-review-of-super-resolution-and-tumor-detection-techniques-in-medical-imaging/fathimath-safana-c-k
Prediction of Lung Cancer Using Image Processing Techniques: A Reviewaciijournal
Prediction of lung cancer is most challenging problem due to structure of cancer cell, where most of the
cells are overlapped each other. The image processing techniques are mostly used for prediction of lung
cancer and also for early detection and treatment to prevent the lung cancer. To predict the lung cancer
various features are extracted from the images therefore, pattern recognition based approaches are useful
to predict the lung cancer. Here, a comprehensive review for the prediction of lung cancer by previous
researcher using image processing techniques is presented. The summary for the prediction of lung cancer
by previous researcher using image processing techniques is also presented.
Prediction of Lung Cancer Using Image Processing Techniques: A Reviewaciijournal
Prediction of lung cancer is most challenging problem due to structure of cancer cell, where most of the
cells are overlapped each other. The image processing techniques are mostly used for prediction of lung
cancer and also for early detection and treatment to prevent the lung cancer. To predict the lung cancer
various features are extracted from the images therefore, pattern recognition based approaches are useful
to predict the lung cancer. Here, a comprehensive review for the prediction of lung cancer by previous
researcher using image processing techniques is presented. The summary for the prediction of lung cancer
by previous researcher using image processing techniques is also presented.
BREAST CANCER DIAGNOSIS USING MACHINE LEARNING ALGORITHMS –A SURVEYijdpsjournal
Breast cancer has become a common factor now-a-days. Despite the fact, not all general hospitals
have the facilities to diagnose breast cancer through mammograms. Waiting for diagnosing a breast
cancer for a long time may increase the possibility of the cancer spreading. Therefore a computerized
breast cancer diagnosis has been developed to reduce the time taken to diagnose the breast cancer and
reduce the death rate. This paper summarizes the survey on breast cancer diagnosis using various machine
learning algorithms and methods, which are used to improve the accuracy of predicting cancer. This survey
can also help us to know about number of papers that are implemented to diagnose the breast cancer.
Decomposition of color wavelet with higher order statistical texture and conv...IJECEIAES
Gastrointestinal cancer is one of the leading causes of death across the world. The gastrointestinal polyps are considered as the precursors of developing this malignant cancer. In order to condense the probability of cancer, early detection and removal of colorectal polyps can be cogitated. The most used diagnostic modality for colorectal polyps is video endoscopy, but the accuracy of diagnosis mostly depends on doctors' experience that is crucial to detect polyps in many cases. Computer-aided polyp detection is promising to reduce the miss detection rate of the polyp and thus improve the accuracy of diagnosis results. The proposed method first detects polyp and non-polyp then illustrates an automatic polyp classification technique from endoscopic video through color wavelet with higher-order statistical texture feature and Convolutional Neural Network (CNN). Gray Level Run Length Matrix (GLRLM) is used for higher-order statistical texture features of different directions (Ɵ=0 0 , 45 0 , 90 0 , 135 0 ). The features are fed into a linear support vector machine (SVM) to train the classifier. The experimental result demonstrates that the proposed approach is auspicious and operative with residual network architecture, which triumphs the best performance of accuracy, sensitivity, and specificity of 98.83%, 97.87%, and 99.13% respectively for classification of colorectal polyps on standard public endoscopic video databases.
Statistical Feature-based Neural Network Approach for the Detection of Lung C...CSCJournals
Lung cancer, if successfully detected at early stages, enables many treatment options, reduced risk of invasive surgery and increased survival rate. This paper presents a novel approach to detect lung cancer from raw chest X-ray images. At the first stage, we use a pipeline of image processing routines to remove noise and segment the lung from other anatomical structures in the chest X-ray and extract regions that exhibit shape characteristics of lung nodules. Subsequently, first and second order statistical texture features are considered as the inputs to train a neural network to verify whether a region extracted in the first stage is a nodule or not . The proposed approach detected nodules in the diseased area of the lung with an accuracy of 96% using the pixel-based technique while the feature-based technique produced an accuracy of 88%.
International Journal of Engineering Research and DevelopmentIJERD Editor
Electrical, Electronics and Computer Engineering,
Information Engineering and Technology,
Mechanical, Industrial and Manufacturing Engineering,
Automation and Mechatronics Engineering,
Material and Chemical Engineering,
Civil and Architecture Engineering,
Biotechnology and Bio Engineering,
Environmental Engineering,
Petroleum and Mining Engineering,
Marine and Agriculture engineering,
Aerospace Engineering.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
At the 35th AICC-RCOG Annual Conference in association with FOGSI and MOGS, Dr. Niranjan Chavan, President of MOGS, gave an address on Artificial Intelligence in Gynaecologic Oncology at Taj Lands' End, Bandra, Mumbai on the 6th November 2022
1. BIOS 2008 CONFERENCE : ADVANCED BIOMEDICAL AND CLINICAL DIAGNOSTIC SYSTEMS IV D. VU, M. MUJAT, T. USTUN, D. HAMMER, D. FERGUSON, and N. IFTIMIA PHYSICAL SCIENCES, INC., ANDOVER, MA B. GOLDBERG, P. JILLELLA, and G. TEARNEY MASSACHUSETTS GENERAL HOSPITAL, BOSTON, MA Spectral-domain low coherence interferometry system for fine/core needle biopsy guidance
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5. Introduction to LCI Coherent Source Low-Coherence Source Mirror Displacement Detectod Singla Mirror Displacement Detector l c ~1/ Image courtesy of - J. De Boer, Wellman Center for Photomedicine / 2
13. Results Intensity Map Tissue Differentiation Map Adipose Tissue A = 87%, F = 11.3%, T = 1.7% Full depth scale ~ 1mm
14. Results Intensity Map Tissue Differentiation Map Adipose Tissue A = 7.7%, F = 0%, T = 92.3% Full depth scale ~ 1mm
15. Results Intensity Map Tissue Differentiation Map Adipose Tissue A = 67.1%, F = 32.9%, T = 0% Full depth scale ~ 1mm
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Editor's Notes
Hi, I am here today to talk to you about an optical technique that we believe has a potential to improve the outcome of needle biopsy in breast cancer diagnosis.
And here is the outline of my talk. I will quickly go over the background, motivation, and how we applied LCI to breast tumor needle biopsy. I will briefly introduce our instrumentation and present some preliminary data from the first few clinical studies using our tissue differentiation algorithm.
First let’s talk about how Needle Biopsy is done. As you can see in the photo, a needle is inserted into the breast where a lump is felt. The pathologist then tries to collect a sample from this lump by aspiration. This sample is then spread onto a slide for examination. This procedure is fast, cheap, and less traumatic for the patient. But (click)
as you can imagine, it takes skills and practice in order to collect sample from the correct site to avoid false negative diagnosis. The lump can move around the needle because of the nature of the surrounding tissues in the beast. They’re mostly fat. Because of this the rate of false negative varies a lot from one pathologist to another. It also gets harder when the tumor is smaller. Therefore Ultra sound and CT scan are then brought in to help the pathologist see where he is going with the needle. But this addition equipment is time consuming, expensive, and requires additional trained staff. This is where we believe LCI implementation can be cheaper, less time consuming alternative. It can be integrated directly onto an existing biopsy probe. Not a lot of training will be needed since the instrument will come with a software to report exactly what type of tissue the biopsy probe is in contact with in real time.
LCI is widely used in the medical optics community. It is interferometry method that allows us to collect depth-resolved data at high resolution. It is based on the concept Michelson interferometer where the light source is split into 2 paths by the beam splitter – one to the reference arm and the other to the sample arm, then the reflected light from the 2 paths are then joined and measured at the detector. When the 2 optical paths are equal, interference occurs. What is shown here are signal detected with 2 different types of light sources. A coherent light source will produce an interference signal over a large range of mirror displacements; whereas a low coherent source only produces a signal when the optical path lengths of the 2 arms match within the coherent length of the source - we are talking about microns in resolution. By scanning the mirror to different location, we are able collect signal at different depth of the sample and construct a depth reflectivity profile. With the advancement of fiber base optics, we are able to have the same apparatus that is more compact and portable. This allows us to bring the optical fiber at the sample arm into a needle as I will show you later. Over the year, the method of LCI has been greatly improved such as the implementation of Fourier Domain LCI including spectral domain and swept source. In these new processes, the moving part is removed to improve the speed of the system as well as noise. Our application uses Spectral Domain. (click)
It is the same set up with the difference in a few parts. The scanning reference mirror is replaced by an adjustable mirror to coarsely match the reference and sample optical paths with each other. At the detection end, instead of a point detector as in the case of Time Domain shown previously, we have a spectrometer which spreads the signal into a spectrum. The Fourier transform of this 1 shot will give us the same reflective profile as in Time domain. As you can imagine, this method would greatly improve our data collection rate as well as noise. In order to construct a 2-D profiles which shows the reflectivity cross-section of a sample, we scan the sample arm across the sample. We call this our OCT probe. At the bottom of the slide are a few examples of the different relfectivity profiles of different tissue types – adipose, fibrous, and tumor. The inherent difference allows us to train our software to distinguish between them.
Here’s what our instrument looks like. As I mentioned earlier, that our instrument can be directly integrated into an existing biopsy probe. A fiber is inserted into the needle, then adapters can be made to mate this needle into the syringe of the biopsy probe. With the tip of the fiber being at the very tip of the needle, we are able to detect exactly what the needle is touching. On the right, we have the OCT probe, which has optics and mechanical parts to scan a mirror across the sample so that depth reflectivity profiles can be collected at different location in the sample
As you can see, the system is very compact. (click) There’s the 15” monitor, computer, and opto-electronics system. (click) The reference arm length can be adjusted from the front and optical fiber connection to the sample arm is also accessible from the front in order to switch back and forth between the FNA and OCT probes. (click) Here’s the inside of the Opto-electronics box. We built our own spectrometer as well as the optical delay line (reference) so they are in the end cheaper and capable of acquiring data at a faster rate than a commercial off the shelf. As you can see, the system is very compact. (click) There’s the 15” monitor, computer, and opto-electronics system. (click) The reference arm length can be adjusted from the front and optical fiber connection to the sample arm is also accessible from the front in order to switch back and forth between the FNA and OCT probes. (click) Here’s the inside of the Opto-electronics box. We built our own spectrometer as well as the optical delay line (reference) so they are in the end cheaper and capable of acquiring data at a faster rate than a commercial off the shelf.
The purpose of our clinical study is to develop a library of different tissue types in order to develop an algorithm to distinguish them from each other. This algorithm training set so far included over 70 samples – normal and cancerous – from 7 patients. The algorithm is then validated by another set of samples. The data collection process is as follows. We use our LCI single profile probe to collect signal at a single point which gives us this reflectivity profile. Then we marked the same spot with ink. We take a few OCT scans over the same location to confirm where we were looking at because a 2-D image is easier to visualize than just a reflectivity profile. We then send the marked sample to a pathologist for a histology at the same location.
Onto the data processing procedure. The purpose of this algorithm is to collect as much information as possible about these individual depth reflectivity profiles in order to differentiate the different tissue types.
1300nm to avoid the absorption of blood.
1. How does your optical design improve aberration and MTF (what is MTF?)? The optics is specially designed for our use- reduce cromatic and spherical aberations. The resolution and performance of an optical objective can be characterized by a quantity known as the modulation transfer function (MTF), which is a measurement of the microscope's ability to transfer contrast from the specimen to the intermediate image plane at a specific resolution. Computation of the modulation transfer function is a mechanism that is often utilized by optical manufacturers to incorporate resolution and contrast data into a single specification. 2. I should mention briefly about how the system is also cabaple of acquiring OCT data and include a photo of the probe? In your PPT, you did present the OCT data. Yes- show a picture of the probe and mention that we did both OCT and point measurements with the needle probe to train and test our algorithm 3. So the advantage of the custom camera is the 1500 A-lines speed (you mean A-lines/sec, right?). There’s no off the shelf commercial camera out there that can acquire images that fast? or there is but too expensive? Besides the speed, is there any other advantages? There are off the shelf cameras- but they are expensive (>20K), big, and therefore not suitable for a portable instrument. Also, ours includes galvo sincronization for frame sync. 4. In slide #14, what is the 3 rd plot? Is the PS. However, those slides are orientative only. You will use the ones from Mircea. 5. Why do we always start off explaining the method of TD-LCI even though we don’t use TD anymore but SD. Is it because TD is easier to visualize with the moving ref mirror? And then you jump in to saying SD is now used because... and then you list the advantages (no moving part etc), but how exactly are we able to extract the depth profile from the spectrum using FFT? Should I go into detail of explaining the science behind it (like in the TD case): “Because of the idea of LCI, any signal that is not within the coherence length of the light source is rejected, and because different wavelength of the source travel in tissue at different speed (diffraction) --> constructive interference at each wavelength only occurs at a particular depth location in the tissue. From knowing the specs of our light source (the coherence length), we can calibrate the tissue depth resolution/size to pixel size in the detected spectrum.” Is that right? People in audience might not be familiar with OCT. A short introduction will not hurt. Not much explanation.. there You will show latter the data processing algorithm.
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