the connection between the histograms of grayscale picture from microscope of astrocytoma?
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So Depending on your specimen preparation and staining ie. light microscopy verus electron microscopy or other. you only need to zomm in to the particular cell of interest capture the image. software gives you histogram for that image. whether its grayscale or RGB doesn't really matter. you should be able to use boolean algebra to subtract out the background of physiological cells. you just do it three times for Red Green and BLUe for color images. There are programs that automatically do blob analysis like character recognition.
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This isn't in the form of a graph, but it might help -->http://www.cosmobrain.com/cosmobrain/res/nearstar.html
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go to http://images.google.com type brain
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Great chart can be found here:\n\nhttp://en.wikipedia.org/wiki/Comparison_of_graphics_file_formats
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http://www.google.com/search?hl=en&q=matlab+ocr&btnG=Google+Search
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You might be looking for Regression analysis.\nThis is the statistical technique used to look for correlations between variables.
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maybe u can check these websites, or just simply search in google images:\n\nhttp://employees.csbsju.edu/hjakubowski/classes/ch331/cho/ergolgi.jpeg\n\nhttp://www.eccentrix.com/members/chempics/Slike/cell/Golgi_apparatus.jpg\n\nhttp://micro.magnet.fsu.edu/cells/golgi/golgiapparatus.html \n\nhope this will help :)
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See if these fit\n\n1) bright field\n\n2) cilia\n\n3) contrast? = staining
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I have no idea about the math, but I'm thinking the assumption is that the data are relatively correlated linearly.  There's a lot of math behind the wiki link.
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Take a look at the websites below for more information. I can decode some of that for you. I can't tell you how serious it is...you need to find a doctor who will talk to you and explain things. \n\nLeiomyosarcoma is a rare cancer that affects smooth muscles. Low to intermediate grade is not as bad as advanced, so that part sounds positive. Focal necrosis means there is a part of the tissue sample that shows cells that are dying. I can't tell you what that means in the overall picture. The mitotic count is a measure of how many cells in the sample are dividing. HPF means high power field. I'm taking this to mean that when they looked under a high power microscope, 2-3 out of 10 cells were dividing. I can't give you a reference number for what is normal, but in general, more cells dividing would indicate the tumor is growing faster than a low number. \n\nImmunohistochemistry means staining the cells in the sample with markers to see what kind of cells they are. CD34 is a marker for undifferentiated cells. S-100 is another marker, but I can't tell you exactly what it indicates. The presence or absence of markers can mean either good or bad news, but I don't know exactly how these are used here and I don't want to guess.\n\nThat doesn't really answer your questions, because you really need a doctor who knows this case to tell you what to expect. I hope that makes it a little less mysterious, but I'm afraid it's not much help. It makes me sad to think that someone would just hand you this report and not give you some help in understanding what is going on. Sometimes hospitals have a social worker who is there to translate the medical jargon into things you really want and need to know. Ask if there is someone like this. I hope it's a good outcome. Good luck.
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It's almost impossible to discuss graphics software without first establishing an understanding of the differences between the two major 2D graphic types: bitmap and vector images. This is an important lesson and often a tough one to grasp. If you work with graphics at all, it's bound to come up, so it's an important concept to understand. Let's start by talking about the more common type: bitmap images. \nFacts About Bitmap Images\nBitmap images (also known as raster images) are made up of pixels in a grid. Pixels are picture elements; tiny dots of individual color that make up what you see on your screen. \n\n\nFacts About Bitmap Images\nBitmap images (also known as raster images) are made up of pixels in a grid. Pixels are picture elements; tiny dots of individual color that make up what you see on your screen. \n\n\nAll these tiny dots of color come together to form the images you see. The typical computer monitor has 72 or 96 pixels per inch, depending on your monitor and screen settings. \nTo illustrate this, let's take a look at a typical desktop icon such as the one shown in the image here. The icons on your desktop are typically 32 by 32 pixels. In other words, there are 32 dots of color going in each direction. When combined, these tiny dots form an image. The icon shown in the upper right corner of this example is a typical desktop icon at screen resolution. As you can see, when you enlarge the icon, as I have in this example, you can clearly see each individual square dot of color. Note the that white areas of the background are still individual pixels, even though they appear to be one solid color. \n\nBitmap images are resolution dependent. Resolution refers to the number of pixels in an image and is usually stated as dpi (dots per inch) or ppi (pixels per inch). Bitmap images are displayed on your computer screen at screen resolution: 72 or 96 ppi. However, when printing bitmaps, your printer needs much more image data than a monitor. In order to render a bitmap image accurately, the typical desktop printer needs 150-300 ppi. If you've ever wondered why your 300 dpi scanned image appears so much larger on your monitor, this is why. For more in-depth information about resolution, scanning, and printing bitmap images, refer to my article Getting Started Scanning. \n\nBecause bitmaps are resolution dependent, it's difficult to increase or decrease their size without sacrificing a degree of image quality. When you reduce the size of a bitmap image through your software's resample or resize command, you must throw away pixels. When you increase the size of a bitmap image through your software's resample or resize command, the software has to create new pixels. When creating pixels, the software must estimate the color values of the new pixels based on the surrounding pixels. This process is called interpolation. \n\nDid you notice in the paragraph above, I specifically talked about resizing images "through your software's resample or resize command"? I want to make the distinction between this method of resizing versus zooming in and out, or dragging the edges of your images in a page layout program to resize it. This type of resizing is more accurately called scaling. Scaling an image does not effect the image permanently. In other words, it does not change the number of pixels in the image. However, if you scale a bitmap image to a larger size in your page layout software, you are going to see a definite jagged appearance. Even if you don't see it on your screen, it will be very apparent in the printed image. Scaling a bitmap image to a smaller size doesn't have any effect; in fact, when you do this you are effectively increasing the ppi of the image so that it will print clearer. \n\nCommon bitmap formats include: \n• BMP \n• GIF \n• JPEG, JPG \n• PNG \n• PICT (Macintosh) \n• PCX \n• TIFF \n• PSD (Adobe Photoshop) \n\nPopular bitmap editing
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In statistics, it's the degree of scatter of data.  In physics it's the separation of visible light into colors by refraction or diffraction.
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find it in : http://images.google.co.in/imghp?ie=UTF-8&oe=UTF-8&hl=en&tab=wi&q=
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http://focus.ti.com/lit/ml/slup117/slup117.pdf
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It's what you peer into. There's a diagram here:\n\nhttp://en.wikipedia.org/wiki/Optical_microscope
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Average number of neurons in the brain = 100 billion\nAverage number of glial cells in brain = 10-50 times the number of neurons
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It's all about the Golden Ratio.\n\nhttp://cuip.uchicago.edu/~dlnarain/golden/activity3.htm