2d Biotelemetry For Cancer Diagnosis Biology Essay

Published: 2021-06-20 11:50:05
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Cancer is the second largest deadliest disease with increasing rate of mortality so any small step to eradicate this disease may equivalent to serving something great to the society. Thus in our project, it not only effectively detect the presence of cancer cells but also it reducing the overall time taken for diagnosis by carrying the whole process under biotelemetry. On the other hand, now biotelemetry is mostly used for one dimensional signals thus in our project it extend for transferring two dimensional signals i.e., image if it happen so then complex or time consuming diagnosis process completes in short duration. The telemetry link was provided by Zigbee transceivers and diagnosis was carried with the help of digital image processing technique.
Keywords : MRI Process, ZIGBEE, Segmentation, BI RADS, MASS
The human race is blessed with all short of things from the environment they are living to the technologies they created. But not all blessings blessed them, some of them changed into curse because of human activities. One such change is due to the adopting easy way of living[1]. This human mentality not only create mental illness but also affect physical health this human life style bring out many deadly diseases and increase the existing diseases to the vast in number for example cancer. Cancer is the second largest deadly diseases that spoil the human race with increasing mortality rate every year. All human parts are prone to this disease. The most commonly affected parts includes brain, lugs and breast among which breast cancer is not at all taken as serious matter over a past decade but a welcoming news is that that social awareness of this increases among the people in recent past[2].
Breast cancer, how worst is the disease can’t express but a report says that every one among 22 women in India are affected by this disease and this rate tends to increase, and India ranked second largest next to America in this disease. The experts tells us the reason for its wide spread, the first is that due to the stress, food habits and second is that it take much time to detect the early stage itself due to manual transferring of information[3]. The first part is on individual hand but when on considering second we can do something. Thus an effective tool is needed to transfer this file immediately as soon as they capture for diagnosis. On the other hand telemetry provides platform to measure the parameters in distant[4]. Now a days, bio telemetry is widely used for measuring and monitoring the single dimension parameters such as EEG, ECG, temperature and pressure etc. It also used to study some other species anatomy.Thus complex medical diagnosis of breast cancer process carried in a less time using 2D-bio-telemetry.
Figure 1: Block diagram of Diagonsis
Magnetic resonance imaging (MRI), or nuclear magnetic resonance imaging (NMRI), is primarily a medical imaging technique most commonly used in radiology to visualize detailed internal structure and limited function of the body. MRI provides much greater contrast between the different soft tissues of the body than computed tomography (CT) does, making it especially useful in neurological (brain), musculoskeletal, cardiovascular, and ontological (cancer) imaging. Unlike CT, it uses no ionizing radiation, but uses a powerful magnetic field to align the nuclear magnetization of (usually) hydrogen atoms in water in the body. Radio frequency (RF) fields are used to systematically alter the alignment of this magnetization, causing the hydrogen nuclei to produce a rotating magnetic field detectable by the scanner[5]. This signal can be manipulated by additional magnetic fields to build up enough information to construct an image of the body.
One of the most recent advances in x-ray mammography is digital mammography. Digital (computerized) mammography is similar to standard mammography in that x-rays are used to produce detailed images of the breast. Digital mammography uses essentially the same mammography system as conventional mammography, but the system is equipped with a digital receptor and a computer instead of a film cassette. Several studies have demonstrated that digital mammography is at least as accurate as standard mammography[6].
During the procedure, the breast is compressed by a dedicated mammography machine to even out the tissue, to increase image quality, and to hold the breast still (preventing motion blur). Both front and side images of the breast are taken. Deodorant, talcum powder or lotion may show up on the X-ray as calcium spots, and women are discouraged from applying these on the day of their investigation.
Until some years ago, mammography was typically performed with screen-film cassettes. Now, mammography is undergoing transition to digital detectors, known as Full Field Digital Mammography (FFDM). This progress is some years later than in general radiology.
This is due to several factors:
the higher resolution demands in mammography,
significantly increased expense of the equipment,
The fact that digital mammography has never been shown to be superior to film-screen mammography for the diagnosis of breast cancer.
While mammography is the only breast cancer screening method that has been shown to save lives, it is not perfect. Estimates of the numbers of cancers missed by mammography are usually around 10%–30%. This means that of the 350 per 100,000 women who have breast cancer, about 35-70 will not be seen by mammography. Reasons for not seeing the cancer include observer error, but more frequently it is because the cancer is hidden by other dense tissue in the breast and even after retrospective review of the mammogram, cannot be seen. Furthermore, one form of breast cancer, lobular cancer, has a growth pattern that produces shadows on the mammogram which are indistinguishable from normal breast tissue.
It is used for communication purpose to transmit and receive messages. ZigBee technology is a low data rate, low power consumption, low cost; wireless networking protocol targeted towards automation and remote control applications. IEEE 802.15.4 committee started working on a low data rate standard a short while later. Then the ZigBee Alliance and the IEEE decided to join forces and ZigBee is the commercial name for this technology. The name ZigBee is said to come from the domestic honeybee which uses a zigzag type of dance to communicate important information to other hive members. This communication dance (the "ZigBee Principle") is what engineers are trying to emulate with this protocol a bunch of separate and simple organisms that join together to tackle complex tasks.
Figure 2: ZigBee Diagram
Figure 3: Black Pin Diagram
The ADSP-BF531/ADSP-BF532/ADSP-BF533 Analog Devices, Inc./Intel Micro Signal Architecture (MSA).Black fin processors combine a dual-MAC state-of-the-art signal processing engine, the advantages of a clean, orthogonal RISC like microprocessor instruction set, and single instruction, multiple data (SIMD) multimedia capabilities into a single instruction set architecture. The ADSP-BF531/ADSP-BF532/ADSP-BF533 processors are completely code and pin-compatible, differing only with respect to their performance and on-chip memory. By integrating a rich set of industry-leading system peripherals and memory, Black fin processors are the platform of choice for next generation applications that require RISC-like programmability, multimedia support, and leading-edge signal processing in one integrated package.
The ADSP-BF531/ADSP-BF532/ADSP-BF533 processors contain a rich set of peripherals connected to the core via several high bandwidth buses, providing flexibility in system configuration as well as excellent overall system performance (see the functional block diagram in Figure). The general purpose Peripherals include functions such as UART, timers with PWM (pulse-width modulation) and pulse measurement capability, general-purpose I/O pins, a real-time clock, and a watchdog timer.
This set of functions satisfies a wide variety of typical system support needs and is augmented by the system expansion capabilities of the part. In addition to these general purpose peripherals, the processors contain high serial and parallel ports for interfacing to a variety of audio, video, and modem codec functions; an interrupt controller for flexible Management of interrupts from the on-chip peripherals or external sources; and power management control functions to tailor the performance and power.
There is also a separate memory DMA channel dedicated to data transfers between the processor’s various memory spaces, including external SDRAM and asynchronous memory. Multiple on-chip buses running at up to 133 MHz provide enough bandwidth to keep the processor core running along with activity on all of the on-chip and external peripherals.
The ADSP-BF531/ADSP-BF532/ADSP-BF533 processors view memory as a single unified 4G byte address space, using 32-bit addresses. All resources, including internal memory, external memory, and I/O control registers, occupy separate sections of this common address space. The memory portions of this Address space are arranged in a hierarchical structure to provide a good cost/performance balance of some very fast, low latency on-chip memory as cache or SRAM, and larger, lower cost and performance off-chip memory systems
The processors have three blocks of on-chip memory that provide high bandwidth access to the core. The first block is the L1 instruction memory, consisting of up to 80K bytes SRAM, of which 16K bytes can be configured as a four way set-associative cache. This memory is accessed at full processor speed.The second on-chip memory block is the L1 data memory, consisting of one or two banks of up to 32K bytes. The memory banks are configurable, offering both cache and SRAM functionality. This memory block is accessed at full processor speed. The third memory block is a 4K byte scratchpad SRAM, which runs at the same speed as the L1 memories, but is only accessible as data SRAM and cannot be configured as cache memory.
External memory is accessed via the external bus interface unit (EBIU). This 16-bit interface provides glue less connection to a bank of synchronous DRAM (SDRAM) as well as up to four banks of asynchronous memory devices including flash, EPROM, ROM, SRAM, and memory mapped I/O devices. The PC133-compliant SDRAM controller can be programmed to interface to up to 128M bytes of SDRAM. The SDRAM controller allows one row to be open for each internal SDRAM bank, for up to four internal SDRAM banks, improving overall system performance.
Black fin processors do not define a separate I/O space. All resources are mapped through the flat 32-bit address space. On-chip I/O devices have their control registers mapped into memory mapped registers (MMRs) at addresses near the top of the 4G byte address space. These are separated into two smaller Blocks, one containing the control MMRs for all core functions, and the other containing the registers needed for setup and control of the on-chip peripherals outside of the core.
The ADSP-BF531/ADSP-BF532/ADSP-BF533 processors contain a small boot kernel, which configures the appropriate peripheral for booting. If the processors are configured to boot from boot ROM memory space, the processor starts executing from the on-chip boot ROM. For more information, Each event type has an associated register to hold the return address and an associated return-from-event instruction. When an event is triggered, the state of the processor is saved on the supervisor stack.
The ADSP-BF531/ADSP-BF532/ADSP-BF533 processors have multiple, independent DMA channels that support automated data transfers with minimal overhead for the processor core. DMA transfers can occur between the processor’s internal memories and any of its DMA-capable peripherals. Additionally, DMA transfers can be accomplished between any of the DMA-capable peripherals and external devices connected to the external memory interfaces, including the SDRAM controller and the asynchronous memory controller. DMA-capable peripherals include the SPORTs, SPI port, UART, and PPI. Each individual DMA-capable peripheral has at least one dedicated DMA channel.
Successful treatment of breast cancer depends on early detection and diagnosis of breast abnormalities and lesions. Mammography is the best available examination for the detection of early signs of breast cancer such as masses, calcifications, bilateral asymmetry and architectural distortion. Because of the limitations of human observers, computers have major role in detecting early signs of cancer. Wide range of features that define abnormalities and the fact that they are often indistinguishable from the surrounding tissue makes the computer-aided detection and diagnosis of breast abnormalities a challenge. This chapter discusses breast lesions and their features also this chapter briefly presents some of the developed computer-aided detection and diagnosis methods for each lesion.
The ACR (American College of Radiology) Breast Imaging Reporting and Data System (BI-RADS®) suggest a standardized method for breast imaging reporting. Terms have been developed to describe breast density, lesion features and lesion classification. Depending on the amount of fibro glandular tissue, breast tissue seen on mammogram can be divided into four categories.The breast is almost entirely fat when there is less than 25% fibro glandular tissue. Scattered fibro glandular dense breast tissue has between 25% And 50% fibro glandular tissue and heterogeneously dense breast tissue has between 51% and 75% fibro glandular tissue. When the breast is consisting of more than 75% fibro glandular tissue the breast is extremely dense. In the latter case sensitivity of mammography exam is decreased and the diagnosis of malignant lesions is more difficult. Many lesions (masses, calcifications, architectural distortion and bilateral asymmetry) are defined with wide range of features. The features determine lesions shape, size, distribution, margins etc. Some of the lesions can be easily overlooked because of the poor feature visibility. One of the problems that appear in diagnosis of malignant lesions is incorrect classification of Lesions. Final assessment and classification of mammograms is made using ACR BI-RADS categories. A negative diagnostic examination is one that is negative, with a benign or probably benign finding (BI-RADS 1, 2 or 3) and a positive Diagnostic examination is one that requires a tissue diagnosis (BI-RADS 4 or 5) or the one with biopsy proof of malignancy (BI-RADS 6). If the finding can Not be assessed, an additional imaging evaluation and/or prior mammograms are needed for comparison (BI-RADS 0).
Figure 4: Examples of mammograms, each of different category of breast tissue: (a) fat breast tissue, (b) scattered fibro glandular dense breast tissue, (c) heterogeneously dense breast tissue and (d) extremely dense breast tissue
A mass is defined as a space occupying lesion seen in at least two different projections. If a potential mass is seen in only a single projection it should be called 'Asymmetry' or 'Asymmetric Density' until its three-dimensionality is confirmed. Masses have different density (fat containing, low density, is dense, high density), different margins (circumscribed, micro lobular, obscured, indistinct, speculated) and different shape (round, oval, lobular, irregular). Fat-containing radiolucent and mixed-density circumscribed lesions are benign, whereas is dense to high-density masses may be of benign or malignant origin. Benign lesions tend to be is dense or of low density, with very well defined margins and surrounded by a fatty halo, but this is certainly not diagnostic of benignancy. The halo sign is a fine radiolucent line that surrounds circumscribed masses and is highly predictive that the mass is benign.
Circumscribed (well-defined or sharply-defined) margins are sharply demarcated with an abrupt transition between the lesion and the surrounding tissue. Without additional modifiers there is nothing to suggest infiltration. A mass with circumscribed margin is shown in Lesions with micro lobular margins have wavy contours. Obscured (erased) margins of the mass are erased because of the superimposition with surrounding tissue. This term is used when the physician is convinced that the mass is sharply-defined but has hidden margins. The poor definition of indistinct (ill defined) margins raises concern that there may be infiltration by the lesion and this is not likely due to superimposed normal breast tissue. The lesions with speculated margins are characterized by lines radiating from the margins of a mass. A lesion that is ill-defined or speculated and in which there is no clear history of trauma to suggest hematoma or fat necrosis suggests a malignant process Shape of a mass can characterize it as benign or malignant. Masses with irregular shape usually indicate malignancy regularly shaped masses such as round and oval very often indicate a benign change.
Figure 5:Examples of (a) circumscribed mass and (b) speculated mass
As it is already said, a typical benign mass has a round, smooth and well circumscribed boundary. On the other hand, a malignant tumor usually has a speculated, rough and blurry boundary. However, there exist atypical cases of macrolobulated or speculated benign masses, as well as microlobulated or well-circumscribed malignant tumors. The detection of masses requires the segmentation of all possible suspicious regions, which may then be subjected to a series of tests to eliminate false positives. Masses can have a range of sizes. Cancerous lesions are stochastic biologic phenomena that manifest in images as having various structures occurring at different sizes and over ranges of spatial scales. The boundaries of masses require a localized approach, although the sharpness and hence the scales of interpretation of the lesion boundaries, can vary considerably. Moreover, the Speculations that are associated with many cancerous lesions occur with different widths, lengths and densities, which suggest that their characterization will require analysis over scales.
Some of the researchers have used texture features to discriminate between mass and normal tissue. Others have defined a number of features that were designed to capture image characteristics like intensity, is-density, location and contrast. Most diagnosis algorithms (CADx) begin with a region of interest (ROI) containing a suspicious mass. In the preprocessing step, the mass is segmented from the background normal tissue. Then the features that capture the difference between malignant and benign masses are extracted. Most features are designed to capture the shape and margin characteristics of masses. These features can be organized into morphologic features and texture features. Finally, masses are classified as malignant or benign. Some researchers have also proposed classification of masses into other categories, such as round, nodular or stellate, or such as fibro adenoma, cyst, or cancer.
Calcifications are tiny granule like deposits of calcium and are relatively bright (dense) in comparison with the surrounding normal tissue. Calcifications detected on mammogram are important indicator for malignant breast disease. Unfortunately, calcifications are also present in many benign changes. Malignant calcifications tend to be numerous, clustered, small, varying in size and shape, angular, irregularly shaped and branching in orientation. Benign calcifications are usually larger than calcifications associated with malignancy. They are usually coarser, often round with smooth margins, smaller in number, more diffusely distributed, more homogeneous in size and shape and are much more easily seen on a mammogram. One of the key differences between benign and malignant calcifications is the roughness of their shape.
Typically benign calcifications are skin calcifications, vascular calcifications, coarse popcorn-like calcifications, large rod-like calcifications, round calcifications, lucent-centered calcifications, eggshell or rim calcifications, milk of calcium calcifications, suture calcification and dystrophic calcifications. Malignancy suspicious calcifications are amorphous and coarse heterogeneous calcifications. Malignancy highly suspicious calcifications are fine pleomorphic, Fine-linear and fine linear-branching calcifications.
The use of computers in processing and analyzing biomedical images allows more accurate diagnose by a radiologist. Humans are susceptible to committing errors and their analysis is usually subjective and qualitative. Objective and quantitative analysis facilitated by the application of computers to biomedical image analysis leads to a more accurate diagnostic decision by the physician. Computer-aided detection (CADe) is designed to provide the radiologist with visual prompts on Series of mammograms. It works by marking a mammogram with marks that indicate regions where the detection algorithm recognizes a suspicious entity that warrants further investigation, thereby complementing the radiologists' interpretation. Findings in a number of studies have demonstrated that CADe has the ability to detect and prompt mammographic signs of cancer with the potential to increase cancer detection rates by approximately 20% .
If a patient's medical history and radiologist's findings are taken into account, together with computer-aided detection data that provides diagnostic output, a computer-aided diagnosis (CADx) system exists. Sometimes, both computer-aided detection and computer-aided diagnosis are referred to as CAD In most developed CADe and CADx programs, there are some common steps that have to be fulfilled in order to find the suspect lesions. Most detection algorithms consist of two stages. In stage 1, the aim is to detect suspicious lesions at a high sensitivity. In stage 2, the aim is to reduce the number of false positives without decreasing the sensitivity drastically. In some approaches some of the steps may involve very simple methods or be skipped entirely. Most diagnosis algorithms (CADx) begin with a region of interest (ROI) containing the abnormality. The output of a CADx system may be the likelihood of malignancy or a management recommendation. Different research groups have worked on different components of the problem and human interaction may occur at various stages. For example, many CADx algorithms start with manually segmented ROIs.
Figure 6 : Diagonsis of CAD Techniques
In the preprocessing step the breast is segmented in order to limit the search for abnormalities without undue influence from the background of the mammogram and some filtering or normalization is accomplished in order to improve the quality of the image and reduce the noise. The next step, feature extraction is one of the most important factors that affect the CAD performance. Basically, researchers Have investigated two types of features: those traditionally used by radiologists (gradient-based, intensity-based and geometric features) and high order features that may not be as intuitive to radiologists (e.g. texture features). Critical issue in CAD design is the choice of the best set of features for detecting or classifying the suspect lesions. The whole set of features may include redundant or irrelevant information. One feature taken alone might not be significant for classification but might be very significant if combined with other features. In order to decide which features are best suited for classification, feature selection is used. Feature selection is defined as selecting a smaller feature Subset of size m from a set of d features that leads to the largest value of some classifier performance function. Finally, a classification (false-positive reduction) step is preformed, where on the basis of the mentioned features false signals are separated from the suspect lesions by means of a classifier. In the other words, the candidate lesions are first located and then further analyzed in a feature analysis and classification phase to determine the final classification of each candidate.
The Simulated Results are done by using the MATLAB.
Thus to participate in the medical world using the electronics we did an effective project for wireless transmission of medical image by 2D telemetry and to focus on the cancer disease. We processed the tumor identification by MATLAB programming which helps the doctor for diagnosis of this disease. By our project we think we are serving to society
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