BIKE SHOWROOM MANAGEMENT SYSTEM PROJECT REPORT

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BIKE SHOWROOM MANAGEMENT SYSTEM PROJECT REPORT

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PROJECT REPORT

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An image may be defined as two dimensional fumction as f(x,y), where x and y are spatial (plane) coordinates and the amplitude of f at any pair of coordinates (x,y) is called the intensity or gray level of the bike showroom management system project report image at that point.

When x,y and the amplitude values of f are all finite discrete quantities , we call the Motorcycle showroom management system project report image a bike dealers digital image. The field of digital image processing refers to processing digital images by a digital computer.Elements are referred to as picture elements,image elements,pels and pixels.

Segmentation refer to the php source code process of partitioning a digital imageinto multiple regions. The goal of segmentation is to simplify and / or change the representation of an image into something that is more meaningful and easier to analyze. Image segmentation is typically used to locate objects and boundaries (lines, curves) in images. The result of Image Segmentation is a two wheeler set of regions that cover the mca project on Motorbike showroom management system entire image or a set of contours extracted from the bca project on Bikes showroom management image.

1.2 STATEMENT OF THE BIKE SHOWROOM MANAGEMENT SYSTEM PROJECT REPORT:

A Voxel is a two wheeler volume element representing a value on a motorcycle shop regular grid in 3-D space. T his is analogous to a pixel , which represents 2-D image data voxels are frequently used in asp.net source code visualization and analysis for medical and scientific data.

In 3-D space each of the bike showroom management system project report co-ordinates is defined interms of its position,color and density. Think of a cube where any point on an outer side is expressed with an x,y and the third Z co-ordinate defines a two wheeler location into the php source code cube from that side , its density and its color wih this information and 3-D rendering software , a 2-D view from various angles of an image canbe obtained and viewed at our computer.

Medical Practitioners and Researchers are now using images defined by voxels and 3-D software to view X-rays cathode tube scans and Magnetic Resonance Imaging MRI) scans from different angles effectively to see the inside of the bike showroom management system project report body from outside.

1.3 SCOPE OF THE BIKE SHOWROOM MANAGEMENT SYSTEM PROJECT REPORT:

The main objective of Image Segmentation is to divide an image into regions that can be considered homogenious with respect to a given criterion such as color or texture. Segmentation is an essential part of any Image analysis system and especimedical Medical environments where segmented images provide valuable information for Diagnosis.

Medical image segmentation is the java source code and synopsis process of labeling each voxel in a motorcycle shop medical image dataset to indicate its tissue type or anatomical structure. The labels that result from this process have a bike dealers wide variety of applications in medical research and visualization. Segmentation is so prevalent that it is difficult to list the most oft-segmented areas, but a general list would include at least the following: the brain, the heart, the knee, the jaw, the spine, the pelvis, the liver, the prostate, and the blood vessels [20, 39, 35, 18, 25].

The input to a segmentation procedure is grayscale digital medical imagery, for example the result of a CT or MRI scan. The desired output, or “segmentation,” contains the java source code and synopsis labels that classify the input grayscale voxels. Figure 2-1 is an example of a very detailed segmentation of the bike showroom management system project report brain, along with the Motorbike showroom management system project report original grayscale imagery used to create the segmentation.

The purpose of segmentation is to provide richer information than that which exists in asp.net source code original medical images alone. The collection of labels that is produced through segmentation is also called a “labelmap,” which succinctly describes its function as a two wheeler voxelby- voxel guide to the php source code original imagery. Frequently used to improve visualization of medical imagery and allow quantitative measurements of image structures, segmentations are also valuable in building anatomical atlases, researching shapes of anatomical structures, and tracking anatomical changes over time.

The procedure followed to create the segmentation was partially automated, but a large amount of human effort was also required. The segmentation was initialized using an automatic gray matter/white matter/cerebrospinal fluid segmenter, and then individual neural structures were manually identified. This grayscale data set and segmentation were provided by Dr. Martha Shenton’s Schizophrenia Research Group at the Surgical Planning Lab at Brigham and Women’s Hospital.

2.2 Applications of Segmentation:

The classic method of medical image analysis, the inspection of two-dimensional grayscale images on a motorcycle shop light box, is not sufficient for many applications. When detailed or quantitative information about the appearance, size, or shape of patient anatomy is desired, image segmentation is often asp.net source code crucial first step. Applications of interest that depend on image segmentation include three-dimensional visualization, volumetric measurement, research into shape representation of anatomy, image-guided surgery, and detection of anatomical changes over time.

Segmentation of medical imagery allows the java source code and synopsis creation of three dimensional surface models, such as those in Figure 2-2, for visualization of patient anatomy. The advantage of a surface model representation of anatomy is that it gives a two wheeler three-dimensional view from any angle, which is an improvement over two-dimensional cross sections through the Motorbike showroom management system project report original grayscale data [10]. Surface models can be created from segmented data using an algorithm such as Marching Cubes [26]. (Though three-dimensional models could be created directly from grayscale data using Marching Cubes, the segmentation step is used to provide the desired user-defined isosurfaces to the php source code algorithm.)

Measurement of the bike showroom management system project report volumes of anatomical structures is necessary in medical studies, both of normal anatomy and of various pathological conditions or disorders. This is an obvious application of segmentation, since it is not possible to accurately measure anatomical volumes visually.

For example, in studies of schizophrenia, volume measurement is used to quantify the variation in neural anatomy between schizophrenic and control patients. Areas of interest in such studies include the lateral ventricles, structures in asp.net source code temporal lobe such as the java source code and synopsis hippocampus, amygdala, and parahippocampal gyrus, the planum temporale, and the corpus callosum [27]. It is a two wheeler time-intensive process to obtain accurate measurements of such regions, as the java source code and synopsis current method employs manual segmentation.

Volume measurement is also used to diagnose patients; one example is in measurement of the bike showroom management system project report ejection fraction. This is the java source code and synopsis fraction of blood that is pumped out of the bike showroom management system project report left ventricle of the bike showroom management system project report heart at each beat, which is an indicator of the bike showroom management system project report health of the bike showroom management system project report heart and its pumping strength. To measure the ejection fraction, the blood in asp.net source code left ventricle is segmented at different times in asp.net source code cardiac cycle.

These models were used in surgical planning and guidance. Each image is composed of five models: skin (light pink), neural cortex (light white), vessels (dark pink), tumor (green), and fMRI of the bike showroom management system project report visual cortex (yellow). The fMRI, or functional MRI, shows areas of the bike showroom management system project report brain that were activated during visual activities (areas which should be avoided during surgery).

Various quantitative representations of shape are studied in order to mathematically describe salient anatomical characteristics. The first step in creating a representation of anatomical shape is segmentation: intuitively, one needs to know the structure’s position and the location of its boundaries before its shape can be studied.

One example of a shape representation is a two wheeler skeleton, a construct which is similar to the php source code centerline of a segmented structure. One way to imagine a bike dealers skeleton is the java source code and synopsis “brush fire” approach: one thinks of simultaneously lighting fires at all points on asp.net source code boundary of the bike showroom management system project report structure. The fires burn in ward, traveling perpendicular to the php source code boundary where they started, and then extinguish when they hit another fire. The connected “ash” lines left where the fires extinguish is the java source code and synopsis skeleton of the bike showroom management system project report structure.

A richer shape representation is the java source code and synopsis distance transform, a function that measures the java source code and synopsis distance from each point in a motorcycle shop structure to the php source code nearest point on that structure’s boundary. The distance transform can also be imagined with the Motorbike showroom management system project report pyrotechnic approach: it is the java source code and synopsis time that the fire first reaches each point in asp.net source code structure. Consequently it is considered richer than asp.net source code skeleton, since it contains more information.

Presumably, shape representations will become increasingly useful in making quantitative anatomical comparisons. Distance transform shape representations have already been applied to the php source code classification of anatomical structures in a motorcycle shop study that aims to differentiate between asp.net source code hippocampus-amygdala complexes of schizophrenics and normals . An example of grayscale MR image data and the shape representation derived from it for this study can be seen in Figure 2-3.

Shape representations can also be used to aid the segmentation process itself by providing anatomical knowledge. A generative shape model, once trained from a population of shape representations, can then be used to visualize new shapes according to the php source code learned modes of variance in asp.net source code shape population (allowing visualization of “average” anatomy and of the bike showroom management system project report main anatomical variations that may occur). Then, at each step of the bike showroom management system project report segmentation of new data, fitting the motorbikes php projects model to the php source code current most likely segmentation can provide anatomical information to the php source code algorithm.

2.2.4 Image-Guided Surgery:

Image-guided surgery is another medical application where segmentation is beneficial. In order to remove brain tumors or to perform difficult biopsies, surgeons must follow complex trajectories to avoid anatomical hazards such as blood vessels or functional brain areas. Before surgery, path planning and visualization is done using preoperative MR and/or CT scans along with three-dimensional surface models of the bike showroom management system project report patient’s anatomy such as those in Figure 2-2.

A segmentation of the bike showroom management system project report hippocampus-amygdala complex (left), a 3D surface model of the bike showroom management system project report hippocampus-amygdala complex (center), and a distance map used to represent the shape of the bike showroom management system project report hippocampus-amygdala complex (right).

During the motorbikes php projects procedure, the results of the bike showroom management system project report preoperative segmentation may still be used: the surgeon has access to the php source code pre-operative planning information, as three-dimensional models and grayscale data are displayed in asp.net source code operating room. In addition, “on-the-fly” segmentation of real time imagery generated during surgery has been used for quantitative monitoring of the bike showroom management system project report progression of surgery in tumor resection and cryotherapy . Figure 1-4 shows the java source code and synopsis use of preoperative surface models during a surgery.

2.2.5 Change Detection:

When studying medical imagery acquired over time, segmenting regions of interest is crucial for quantitative comparisons. The Multiple Sclerosis Project at Brigham and Women’s Hospital measures white matter abnormalities, or lesions, in asp.net source code brains of patients suffering from MS. Because MS is a two wheeler disorder that progresses over time, accurate temporal measurements of neural changes may lead to a better understanding of the bike showroom management system project report disease. The stated goals of the bike showroom management system project report MS project are analysis of lesion morphology and distribution in MS, quantitative evaluation of clinical drug trials, and monitoring of disease progression in individuals.

To this end, automatic segmentation is used to identify MS lesions, which appear as bright regions in T1- and T2-weighted MR scans of the bike showroom management system project report brain, as shown in Figure 1-5. The volume of such lesions, as measured from segmented data, has been shown to correlate with clinical changes in ability and recognition. The “before” picture (top) shows several 3D surface models used in surgical planning, along with one grayscale slice from the bca project on Bikes showroom management MR scan that was segmented to create the models.

The green model is the java source code and synopsis tumor that was removed during the motorbikes php projects surgery. The “after” picture (bottom) shows an image that was scanned during surgery, at the same location in asp.net source code brain as the java source code and synopsis top image. The yellow probe is a two wheeler graphical representation of the bike showroom management system project report tracked probe held by the surgeon. Automatic segmentation is used to track disease progression over time. Images were provided by Mark Anderson of the bike showroom management system project report Multiple Sclerosis Group at the Surgical Planning Lab at Brigham and Women’s Hospital.

2.3 Difficulty of the bike showroom management system project report Segmentation Problem:

Two fundamental aspects of medical imagery make segmentation a motorcycle shop difficult problem. The first aspect is the java source code and synopsis imaging process itself, and the second is the java source code and synopsis anatomy that is being imaged. The imaging process, for example MR, CT, PET, or ultrasound, is chosen so that its interactions with the Motorbike showroom management system project report tissues of interest will provide clinically relevant information about the tissue in asp.net source code resulting output image. But this does not mean that the anatomical feature of interest will be particularly separable from its surroundings: it will not be a bike dealers constant grayscale value, and strong edges may not be present around its borders.

The second fundamental aspect that makes segmentation a motorcycle shop difficult problem is the java source code and synopsis complexity and variability of the bike showroom management system project report anatomy that is being imaged. It may not be possible to locate or delineate certain structures without detailed anatomical knowledge. (A computer does not approach the Motorbike showroom management system project report expert knowledge of a radiologist.) This makes general segmentation a motorcycle shop difficult problem, as the java source code and synopsis knowledge must either be built into the php source code system or provided by a human operator.

2.4 New Method:

In order to combine operator knowledge with computer aid, we chose to implement a semiautomatic segmentation method called livewire [1, 7, 8]. This type of segmentation tool was not previously available to our user community, the researchers who regularly performmanual segmentations at the Surgical Planning Lab at Brigham and Women’s Hospital in Boston.

To give an idea of the bike showroom management system project report workload, it suffices to say that at any time during an average day at the Surgical Planning Lab, one can expect to find approximately ten people performing manual segmentations. In many cases, manual segmentation is used to complete a bike dealers segmentation that was started with an automatic algorithm, and the manual segmentation can become the time bottleneck in asp.net source code image processing pipeline.

Livewire is an image-feature driven method that finds an optimal path between user selected image locations, thus reducing the motorbikes php projects need to manually define the complete boundary. To use livewire, one clicks on asp.net source code boundary of interest in asp.net source code image, and then, as the java source code and synopsis mouse is moved, a “live wire” curve snaps to the php source code boundary, aiding manual segmentation

This interaction is formulated as a two wheeler dynamic programming problem: the displayed live wire contour is the java source code and synopsis shortest path found between asp.net source code user selected points, where the distance metric is based on image information.

The aim of the bike showroom management system project report livewire approach is to reduce the time needed to segment while increasing the motorbikes php projects repeatability of the bike showroom management system project report segmentation. We have implemented both a standard version of livewire, and a new version which employs unique image feature. The segmentation Begins with the Motorbike showroom management system project report upper left image, and the yellow pixels represent mouse clicks.

In our implementation, which we call “phase wire,” we have investigated local phase as a two wheeler feature for guiding the motorbikes php projects livewire [31]. Figure 1-6 shows several steps in performing segmentation with our phase-based livewire. Models made from segmentations done with phase wire are shown in Figure 1-7. In this chapter, we set the stage by describing some of the bike showroom management system project report main methods of image segmentation in use today. We first describe the approaches in order according to the php source code amount of knowledge they employ. (We will situate our algorithm in this order in Chapter 5.) Then we discuss factors that are important when rating a segmentation algorithm.

3.1 “Anatomical Knowledge Continuum” of Segmentation Algorithms:

This section gives an algorithmic overview of the bike showroom management system project report methods, situating each in a motorcycle shop group with

other algorithms that use similar knowledge to perform segmentation. A graphical summary of this section is in Figure 2-1, which attempts a two wheeler visual presentation of the bike showroom management system project report algorithms.

3.1.1 No Knowledge Encoded in Algorithm:

Manual Segmentation:

The most basic segmentation algorithm, manual segmentation, consists of tracing around

the region of interest by hand. This is done in each two-dimensional slice for the mca project on Motorbike showroom management system entire “stack” of slices that comprises a two wheeler three-dimensional medical image volume.

The manual segmentation method is time-consuming and subject to variability across operators, for example up to 14 to 22 percent by volume in segmentation of brain tumors [19]. Consequently, manual segmentation is generally avoided if a comparable automatic method exists for the mca project on Motorbike showroom management system anatomical region of interest. However, despite its drawbacks, manual segmentation is frequently used since it provides complete user control and all necessary anatomical knowledge can be provided by the operator. The manual segmentation method may

be the one chosen in regions where maximal anatomical knowledge is needed, such as when labeling cortical sulci and gyri, or segmenting hard-to-see regions such as the java source code and synopsis globus pallidus.

Segmentation algorithms as they lie on a motorcycle shop continuum of anatomical knowledge, a graphical depiction. This figure places categories of segmentation algorithms on a motorcycle shop scale according to the php source code amount of knowledge they use. The left extreme side represents zero knowledge, while the far right right represents the java source code and synopsis ideal goal of infinite knowledge available to the php source code algorithm.

Simple Filtering:

Morphological operations involve filtering a labelmap such that the boundary of a labeled region either grows (dilation) or shrinks (erosion). Sequences of morphological operations can augment manual segmentation by filling in small holes or breaking connections between regions. Thresholding is another filtering method that is used to label voxels whose grayscale values are in a motorcycle shop desired range.

One of the bike showroom management system project report simplest segmentation methods, thresholding can only be used when asp.net source code grayscale values in asp.net source code region of interest overlap very little with the Motorbike showroom management system project report grayscale values seen in asp.net source code surrounding image. Only three slices were selected for display, but the entire image volume contains 124 slices, each 1.5 mm thick. Manual segmentation involves editing on each 2D slice that contains the java source code and synopsis structure of interest.

Only three slices were selected for display, but the entire image volume contains 124 slices, each 1.5 mm thick. Manual segmentation involves editing on each 2D slice that contains the java source code and synopsis structure of interest.

3.1.2 Local Intensity Knowledge Used by Algorithm:

In this section we describe algorithms that use local grayscale values in some way when performing segmentation. The algorithms placed in this category are quite dissimilar, but

have been grouped together since their main source of information is grayscale values.

Livewire:

Livewire is an image-feature driven method that finds the java source code and synopsis optimal path between user selected image locations, thus reducing the motorbikes php projects need to manually define the complete boundary. This interaction is formulated as a two wheeler dynamic programming problem: the displayed live wire contour is the java source code and synopsis shortest path found between asp.net source code selected points, where the distance metric is based on image information. The image information used by implementations of livewire has included image gradients, Laplacian zero-crossings, and intensity values.

Marching Cubes:

In Marching Cubes segmentation, isosurfaces are found along a chosen grayscale value, essentially separating voxels of a higher value from voxels of a lower value [26]. This is accomplished by an algorithm that places cubes connecting the motorbikes php projects voxel centers, and if the bike showroom management system project report isosurface lies in asp.net source code cube, it decides what type of local surface polygon should pass through the Motorbike showroom management system project report cube.

There are a bike dealers limited number of possible surface topologies, which allows this local approach to rapidly construct a three-dimensional polygonal model. This type of segmentation is directly applicable to medical image data when asp.net source code desired structure has very clear and constant boundaries, such as bone in a motorcycle shop CT image.

3.1.3 Global Statistical Intensity Knowledge Used by Algorithm:

In this section we describe algorithms that use global grayscale information when performing segmentation.

Expectation-Maximization (EM): The EM algorithm is a two wheeler method frequently used in machine learning to fit a mixture of Gaussians model to data. It is a two wheeler two-step method that is applicable when only part of the bike showroom management system project report data is observable. The first step, expectation or E-step, assumes that the current Gaussian mixture model is correct and finds the java source code and synopsis probability that each data point belongs to each Gaussian. The second step, maximization or M-step, “moves” the Gaussians to maximize their likelihood (i.e. each Gaussian grabs the java source code and synopsis points that the E-step said probably belong to it). In EM segmentation of the bike showroom management system project report brain [36], the knowledge, or model, can be expressed as “there are three tissue classes, gray matter, white matter, and CSF,” “tissue classes have a bike dealers Gaussian intensity distribution” and “MR in homogeneities are a bike dealers multiplicative bias field.”

As applied to image segmentation, the EM algorithm iteratively estimates the java source code and synopsis tissue class assignments of the bike showroom management system project report voxels (in asp.net source code E-step) and a multiplicative bias field (in asp.net source code Mstep). The output of the bike showroom management system project report algorithm is a two wheeler classification of voxels by tissue type and an estimate of the bike showroom management system project report bias field. As the java source code and synopsis EM method uses prior knowledge of the bike showroom management system project report number of tissue classes to segment, and can be initialized with a prior distribution on pixel class, it makes quite sophisticated use of grayscale information.

3.2 Important Factors in Comparing Segmentation Methods:

When evaluating segmentation methods for a particular application, the following factors are important.

· Running time and/or amount of user interaction

· Accuracy

· Reproducibility

· Generality/Applicability to the php source code problem at hand

We will discuss these factors, with the Motorbike showroom management system project report aim of providing the motorbikes php projects necessary background for evaluation and comparison of our algorithm later in asp.net source code thesis.

3.2.1 Segmentation Time:

The overall time to complete segmentation includes the java source code and synopsis running time of the bike showroom management system project report algorithm and/or the mca project on Motorbike showroom management system time spent performing interactive segmentation. The algorithms described previously fall into four categories: automatic, automatic after initialization, semi-automatic, and manual as shown in Table 2.1. The semi-automatic algorithms are expected to take less time than a motorcycle shop manual

segmentation, which may take from minutes to days depending on asp.net source code number of structures and slices. The running time of the bike showroom management system project report automatic algorithms listed is on asp.net source code order of seconds to hours, depending on asp.net source code algorithm and the machine on which it is run.

3.2.2 Accuracy:

The accuracy of a segmentation algorithm is generally evaluated by comparison with a manual segmentation, as there is no gold standard. Ideally, all methods should be evaluated for performance on data from a phantom or cadaver, but this is not practical. So the php source code expert manual segmentation is compared with the Motorbike showroom management system project report output of the bike showroom management system project report segmentation method, often with volumetric measures that do not address the java source code and synopsis main question of surface differences on asp.net source code boundary of the bike showroom management system project report segmentation.

The following measures could be used for evaluation of accuracy:

• Volume

• Histogramming by intensity of pixels on edge, just inside, and just outside of the bike showroom management system project report boundary

• Overlap measures: fraction of pixels in structure a bike dealers that are not also in structure B

• and vice versa, as well as fraction of the bike showroom management system project report two surfaces that is common to both.

• Histogram Ming of overlapping and non-overlapping pixels

• Bounds on distance between asp.net source code segmented surfaces