ChoudharyRajbhandarySutton - Revision history

imported>Psych2012: /* MATLAB Code */

2012-03-22T07:13:35Z

MATLAB Code

← Older revision		Revision as of 07:13, 22 March 2012
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	==MATLAB Code==		==MATLAB Code==
	[[File:HyspexPigOrganMATLAB.zip]]		[[File:HyspexPigOrganMATLAB.zip]]

	~~[[File:organ.mat]]~~

	= Appendix II - Work partition (if a group project) =		= Appendix II - Work partition (if a group project) =

imported>Psych2012: /* MATLAB Code */

2012-03-22T06:22:10Z

MATLAB Code

← Older revision		Revision as of 06:22, 22 March 2012
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	==MATLAB Code==		==MATLAB Code==
	[[File:HyspexPigOrganMATLAB.zip]]		[[File:HyspexPigOrganMATLAB.zip]]

	[[File:organ.mat]]		[[File:organ.mat]]

imported>Psych2012: /* MATLAB Code */

2012-03-22T06:22:00Z

MATLAB Code

← Older revision		Revision as of 06:22, 22 March 2012
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	==MATLAB Code==		==MATLAB Code==
	[[File:HyspexPigOrganMATLAB.zip]]		[[File:HyspexPigOrganMATLAB.zip]]
			[[File:organ.mat]]

	= Appendix II - Work partition (if a group project) =		= Appendix II - Work partition (if a group project) =

imported>Psych2012 at 06:14, 22 March 2012

2012-03-22T06:14:52Z

imported>Psych2012: /* MATLAB Code */

2012-03-22T05:48:22Z

MATLAB Code

← Older revision		Revision as of 05:48, 22 March 2012
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	==MATLAB Code==		==MATLAB Code==
	[[File:~~CodeFile~~.zip]]		[[File:HyspexPigOrganMATLAB.zip]]

	= Appendix II - Work partition (if a group project) =		= Appendix II - Work partition (if a group project) =

imported>Psych2012: /* Future Work */

2012-03-22T05:44:15Z

Future Work

← Older revision		Revision as of 05:44, 22 March 2012
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	= Future Work =		= Future Work =
	*The algorithms developed in this project can be further tested with more data sets. In future tests, we would take extreme care to ensure that the photon illumination of our data sets is uniform, as we believe that the reflectance estimation is compromised by non-uniform illumination of the organ.		*The algorithms developed in this project can be further tested with more data sets. In future tests, we would take extreme care to ensure that the photon illumination of our data sets is uniform, as we believe that the reflectance estimation is compromised by non-uniform illumination of the organ.
	*Imaging the effects of altered blood supply to organs have previously been studied, and ~~these algorithms further this field~~ of ~~study by analyzing the differences between organs before and after~~ ischemia.		*Imaging the effects of altered blood supply to organs have previously been studied. These algorithms can be applied to pre and post-ischemic organ tissues to improve early detection of ischemia.
	*The algorithms developed in this project could be further implemented at the hardware level to apply real-time image processing. This would allow physicians to simply turn these enhancement features on to help differentiate between similar looking organs.		*The algorithms developed in this project could be further implemented at the hardware level to apply real-time image processing. This would allow physicians to simply turn these enhancement features on to help differentiate between similar looking organs.


	= References =		= References =

imported>Psych2012 at 05:39, 22 March 2012

2012-03-22T05:39:47Z

← Older revision		Revision as of 05:39, 22 March 2012
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	A simple approach to use the reflectance values to enhance contrast is to use them as the intensity itself. We are no longer concerned with the photon intensity values, but directly plot the normalized reflectance values (scaled to between 0 and 1 for rendering in MATLAB). Thus for our chosen wavelengths (720, 616 and 480 nm), we directly map the reflectance values to the R, G, and B intensities respectively. Since the wavelength bins are chosen to given the maximum variance between the reflectance values, mapping them to RGB intensities should give us some contrast. The images below show the standard RGB rendering of photon intensities vs the rendering using the raw reflectance values for the 4 organs (from top to bottom, colon, liver, spleen stomach—the left is standard RGB, and the right is the raw reflectances rendered).		A simple approach to use the reflectance values to enhance contrast is to use them as the intensity itself. We are no longer concerned with the photon intensity values, but directly plot the normalized reflectance values (scaled to between 0 and 1 for rendering in MATLAB). Thus for our chosen wavelengths (720, 616 and 480 nm), we directly map the reflectance values to the R, G, and B intensities respectively. Since the wavelength bins are chosen to given the maximum variance between the reflectance values, mapping them to RGB intensities should give us some contrast. The images below show the standard RGB rendering of photon intensities vs the rendering using the raw reflectance values for the 4 organs (from top to bottom, colon, liver, spleen stomach—the left is standard RGB, and the right is the raw reflectances rendered).

	[[File:reflect13.png\|thumb\|center\|1000px\|Figure 15 - Comparison between the image ~~render~~ of conventional RGB values to the ~~PCA wavebands~~ directly mapped to RGB values. The first column shows the values directly rendered to RGB values and the second rows column shows images of PCA mapped to RGB. Organs from top row to bottom are colon, liver, spleen and stomach]]		[[File:reflect13.png\|thumb\|center\|1000px\|Figure 15 - Comparison between the image rendering of conventional RGB values to the raw reflectance values directly mapped to RGB values. The first column shows the values directly rendered to RGB values and the second rows column shows images of PCA mapped to RGB. Organs from top row to bottom are colon, liver, spleen and stomach]]


	This approach need not be done on a pixel by pixel basis. We could ~~schieve~~ some sort of smoothing by dividing the image into a grid and averaging reflectances over each section of the grid. For example the images above actually have the reflectance values averaged over a 2x2 grid. This griding can be potentially used to average out noise from the image at the cost of making it look pixelated. ~~For example, the~~ images using a 5x5 grid are shown below. However averaging out the reflectance values is not beneficial from a contrast point of view and so we stick to the first scenario.		This approach need not be done on a pixel by pixel basis. We could achieve some sort of smoothing by dividing the image into a grid and averaging reflectances over each section of the grid. For example the images above actually have the reflectance values averaged over a 2x2 grid. This griding can be potentially used to average out noise from the image at the cost of making it look pixelated. The images using a 5x5 grid are shown below. However averaging out the reflectance values is not beneficial from a contrast point of view and so we stick to the first scenario.

	[[File:reflect14.png\|thumb\|center\|500px\|Figure 16 - ]]		[[File:reflect14.png\|thumb\|center\|500px\|Figure 16 - Reflectance values averaged over 5x5 grids of pixels and directly mapped to RGB. Top left- Colon, Top right - Liver, Bottom Left - Spleen, Bottom Right - Stomach ]]

	This approach does suffer from the drawback that if certain images are far apart in the RGB space, they are not necessarily perceptually different. In order to quantify and enhance perceptual differences, we had to move into CIELab space.		This approach does suffer from the drawback that if certain images are far apart in the RGB space, they are not necessarily perceptually different. In order to quantify and enhance perceptual differences, we had to move into CIELab space.

imported>Psych2012 at 05:25, 22 March 2012

2012-03-22T05:25:05Z

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	= Conclusions =		= Conclusions =
	* Based on aforementioned studies performed on hyperspectral image data of four pig organs, we have shown that it is possible to ~~increase~~ contrast between pig organs. Our aim was to evaluate various methods to find the ~~high~~ contrast bins, and this was performed by maximum standard deviation and PCA algorithms. Both algorithm showed higher contrast compared to conventional RGB pictures.		Based on aforementioned studies performed on the hyperspectral image data of four pig organs, we have shown that it is possible to render images with enhanced contrast between pig organs. Our aim was to evaluate various methods to find the highest contrast bins, and this was performed by maximum standard deviation and PCA algorithms. Both algorithm showed higher contrast compared to conventional RGB pictures. We also showed that by using representative reflectance, which is specific to different organs, we were able to distort the RGB values in the CIELab colorspace. We saw perceptual difference between organs that would otherwise look similar. We also showed that a correlation algorithm using bilateral filter was successfully able to identity different organs based on the training set. We have provided analysis for selecting 3 or 4 wave bands that could now replace the filters in traditional camera. We have shown from our studies that images obtained from these cameras should provide increased contrast when compared to standard RGB filters. Finally, we have made a Hyperspectral Image Analyzer platform, which allows a user to load images, and use the methods studied in this project to distort and render new images. This tool is useful to study the difference between the original image and the enhanced image, and can be used as a user-friendly educational tool.
	* We also showed that by using representative reflectance -- which is specific to different organs -- we ~~are~~ able to distort the RGB values in the ~~CIE~~ colorspace~~, and produce~~ perceptual difference between organs that would otherwise look similar. We also showed that correlation algorithm using bilateral filter was successfully able to identity different organs based on the training set.
	* We have provided analysis for selecting 3 or 4 wave bands that could now replace the filters in traditional camera. We have shown from our studies that images obtained from these cameras should provide ~~high~~ contrast.
	* We have made a Hyperspectral Image Analyzer platform, which allows user to load images, and use the methods studied in this project to render images. This tool is useful to study the difference between the original image and the enhanced image, and can be used as a user-friendly educational tool.

	= Future Work =		= Future Work =
	* The algorithms developed in this project can be further tested with more data sets. We would take extreme care to ensure that the photon illumination is uniform, as we believe that the reflectance estimation is ~~deteriorated~~ by non-uniform illumination of the organ.		*The algorithms developed in this project can be further tested with more data sets. In future tests, we would take extreme care to ensure that the photon illumination of our data sets is uniform, as we believe that the reflectance estimation is compromised by non-uniform illumination of the organ.
	* ~~Effect~~ of blood supply to organs have previously been studied, and these algorithms ~~can be applied to analyze~~ the ~~difference~~ between organs before and after ischemia.		*Imaging the effects of altered blood supply to organs have previously been studied, and these algorithms further this field of study by analyzing the differences between organs before and after ischemia.
	* The algorithms developed in this project could be further ~~optimized~~ to apply ~~it in~~ real-time image processing ~~so that these algorithms can be implemented in hardware level~~. This would allow physicians to simply turn these enhancement features on~~, and it would~~ help differentiate organs.		*The algorithms developed in this project could be further implemented at the hardware level to apply real-time image processing. This would allow physicians to simply turn these enhancement features on to help differentiate between similar looking organs.


	= References =		= References =

imported>Psych2012 at 05:17, 22 March 2012

2012-03-22T05:17:59Z

← Older revision		Revision as of 05:17, 22 March 2012
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	==Graphic User Interface: Hyperspectral Image Analyzer ==		==Graphic User Interface: Hyperspectral Image Analyzer ==
	Hyperspectral Image Analyzer provides graphical user interface to load hyperspectral images, and compare the results produced by different algorithms. The interface also allows user to select wave bands in the hyperspectral image to be mapped to the standard RGB. In the figure below, speen is being selected, and correlation algorithm is used to render the image by using the wave bands selected from PCA analysis. The original image and rendered images are shown for comparision.		Hyperspectral Image Analyzer provides graphical user interface to load hyperspectral images, and compare the results produced by different algorithms. The interface also allows user to select wave bands in the hyperspectral image to be mapped to the standard RGB. In the figure below, speen is being selected, and correlation algorithm is used to render the image by using the wave bands selected from PCA analysis. The original image and rendered images are shown for comparision.
	[[File:GUI.jpg \|thumb\|center\|650px\| Figure 19 - ]]		[[File:GUI.jpg \|thumb\|center\|650px\| Figure 19 - A snapshot of Hyperspectral Graphic User Interface: GUI with annotation of various features and properties. Images can be loaded and displayed by passing it through different algorithms. The original and rendered images are displayed. The wavebands to be used for selected algorithms can also be manually selected by using the slider.]]

	= Conclusions =		= Conclusions =

imported>Psych2012 at 05:13, 22 March 2012

2012-03-22T05:13:25Z

← Older revision		Revision as of 05:13, 22 March 2012
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	Results based on the aforementioned correlation algorithm is shown in the figure below. Correlation algorithm sucessfully distinguished different test organ images. However, as can been seen from the images, skins and shadows were also recognized as being organs (for example: blue pixel overlay on some part of the skin). This artefact, although reduced by bilateral filter, cannot be completely eliminated because of the non-uniform illumination. When estimating reflectance of the surfaces in the field of view, the photon count from the white tape is taken as the illuminant.		Results based on the aforementioned correlation algorithm is shown in the figure below. Correlation algorithm sucessfully distinguished different test organ images. However, as can been seen from the images, skins and shadows were also recognized as being organs (for example: blue pixel overlay on some part of the skin). This artefact, although reduced by bilateral filter, cannot be completely eliminated because of the non-uniform illumination. When estimating reflectance of the surfaces in the field of view, the photon count from the white tape is taken as the illuminant.
	[[File:correlation.jpg\|thumb\|center\|650px\|Figure 18 - Organ images run through the reflectance correlation algorithm show different organs being shown as different colors. Notice that some dark area and some skin pixels are also rendered as being particular organ. We believe this artifact is due to non uniform illumination of the image.]]		[[File:correlation.jpg\|thumb\|center\|650px\|Figure 18 - Organ images run through the reflectance correlation algorithm show different organs being shown as different colors. Colon, liver, spleen and stomach are shown as blue, red, white and green, respectively. Notice that some dark area and some skin pixels are also rendered as being particular organ. We believe this artifact is due to non uniform illumination of the image.]]

	==Graphic User Interface: Hyperspectral Image Analyzer ==		==Graphic User Interface: Hyperspectral Image Analyzer ==

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	Hyperspectral images contain hundreds of spectral samples from across the electromagnetic spectrum per pixel. Appropriate rendering of such information in the RGB or grayscale monitor can provide extra information about the images which have been exploited in applications such as agriculture, minerology, surveillance and surgery. The spectral bands of the images must first be projected to a lower dimensional space, typically the RGB color space or grayscale of a monitor for the display.		Hyperspectral images contain hundreds of spectral samples from across the electromagnetic spectrum per pixel. Appropriate rendering of such information in the RGB or grayscale monitor can provide extra information about the images which have been exploited in applications such as agriculture, minerology, surveillance and surgery. The spectral bands of the images must first be projected to a lower dimensional space, typically the RGB color space or grayscale of a monitor for the display.

	[[File:figure1.jpg \|right\|thumb\|400px\| Figure 1 - Realization of Hyperspectral Images in 3 dimensions. x and y determine the size of the image being captured. z depends on the number of unique wavelengths bins that ~~are able to be~~ acquired]]		[[File:figure1.jpg \|right\|thumb\|400px\| Figure 1 - Realization of Hyperspectral Images in 3 dimensions. x and y determine the size of the image being captured. z depends on the number of unique wavelengths bins that have been acquired]]
	Biomedical applications of hyperspectral images include real-time and non-invasive diagnosis of skin cancers [http://web.eecs.utk.edu/~kong/research.htm], features to enhance contrast between different organs, and determination of blood concentrations to identify regions of ischemia [http://cdn.intechopen.com/pdfs/9221/InTech-Hyperspectral_imaging_a_new_modality_in_surgery.pdf]. Our aim is to choose three to four spectral bands, which provide maximum contrast, to apply these algorithms in retrofitted conventional cameras.		Biomedical applications of hyperspectral images include real-time and non-invasive diagnosis of skin cancers [http://web.eecs.utk.edu/~kong/research.htm], features to enhance contrast between different organs, and determination of blood concentrations to identify regions of ischemia [http://cdn.intechopen.com/pdfs/9221/InTech-Hyperspectral_imaging_a_new_modality_in_surgery.pdf]. Our aim is to choose three to four spectral bands, which provide maximum contrast, to apply these algorithms in retrofitted conventional cameras.

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	==Method III: Raw Reflectance Value Rendering==		==Method III: Raw Reflectance Value Rendering==
	A simple approach to use the reflectance values to enhance contrast is to use them as the intensity itself. We are no longer concerned with the photon intensity values, but directly plot the normalized reflectance values (scaled to between 0 and 1 for rendering in MATLAB). Thus for our chosen wavelengths (720, 616 and 480 nm), we directly map the reflectance values to the R, G, and B intensities respectively. Since the wavelength bins are chosen to given the maximum variance between the reflectance values, mapping them to RGB intensities should give us some contrast. The images below show the standard RGB rendering of photon intensities vs the rendering using the raw reflectance values for the 4 organs (from top to bottom, colon, liver, spleen stomach—the left is standard RGB, and the right is the raw reflectances rendered).		A simple approach to use the reflectance values to enhance contrast is to use them as the intensity itself. We are no longer concerned with the photon intensity values, but directly plot the normalized reflectance values (scaled to between 0 and 1 for rendering in MATLAB). Thus for our chosen wavelengths of 720, 616 and 480 nm (from [[#Method I: Maximum Standard Deviation and PCA Weighting\|Method I]]), we directly map the reflectance values to the R, G, and B intensities respectively. Since the wavelength bins are chosen to given the maximum variance between the reflectance values, mapping them to RGB intensities should give us some contrast. The images below show the standard RGB rendering of photon intensities vs the rendering using the raw reflectance values for the 4 organs (from top to bottom, colon, liver, spleen stomach—the left is standard RGB, and the right is the raw reflectances rendered).

	[[File:reflect13.png\|thumb\|center\|1000px\|Figure 15 - Comparison between the image rendering of conventional RGB values to the raw reflectance values directly mapped to RGB values. The first column shows the ~~values directly~~ rendered to RGB ~~values~~ and the second ~~rows~~ column shows images of ~~PCA~~ mapped to RGB. Organs from top row to bottom are colon, liver, spleen and stomach]]		[[File:reflect13.png\|thumb\|center\|1000px\|Figure 15 - Comparison between the image rendering of conventional RGB values to the raw reflectance values directly mapped to RGB values. The first column shows the images rendered by mapping photon intensities to RGB and the second column shows images of raw reflectance values mapped to RGB. Organs from top row to bottom are colon, liver, spleen and stomach]]


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	Here, α is empirically found to be 100, (in the code we calculate the reflectance as Radiance/Illuminant, and the values we got were scaled by a factor of 100 from the mean_reflectance values computed in ISET). This simple transformation itself gives us a pretty good contrast between the organs. Figure 17 shows the before (left) and after (right) of the organ tissues and the deltaE values (plotted as contours depicting gradients across the pixels). Note the change in scale of the contour maps in the “after” plot.		Here, α is empirically found to be 100, (in the code we calculate the reflectance as Radiance/Illuminant, and the values we got were scaled by a factor of 100 from the mean_reflectance values computed in ISET). This simple transformation itself gives us a pretty good contrast between the organs. Figure 17 shows the before (left) and after (right) of the organ tissues and the deltaE values (plotted as contours depicting gradients across the pixels). Note the change in scale of the contour maps in the “after” plot.

	[[File:cielab5.png\|thumb\|center\|850px\| Figure 17 - Left: before Right: after Lab transformation of the organ tissues. The deltaE values are plotted as contours depicting gradients across the pixels.]]		[[File:cielab5.png\|thumb\|center\|850px\| Figure 17 - Left: Organ Tissues and deltaE before CIE Lab transformation, the top left plot renders the tissues by mapping the intensities at 720 nm to R, 616 nm to G and 480 nm to B. Right: after Lab transformation of the organ tissues. The deltaE values are plotted as contours depicting gradients across the pixels.]]

	Given that this simple transformation gives such good results, if we had more time, we would have liked to try other non-linear transformations to further improve the quality of the contrast. We believe that going to CIELab has tremendous potential in terms of a quantitative contrast enhancement methodology. However, due to the computation intensive nature of this transformation, we would like to perform the transformations only on sections of the image which have organs present, or in other words, we would like to identify the organs in the image and then proceed towards contrast enhancement in those areas. To achieve this, we use a correlation based algorithm described in the next section.		Given that this simple transformation gives such good results, if we had more time, we would have liked to try other non-linear transformations to further improve the quality of the contrast. We believe that going to CIELab has tremendous potential in terms of a quantitative contrast enhancement methodology. However, due to the computation intensive nature of this transformation, we would like to perform the transformations only on sections of the image which have organs present, or in other words, we would like to identify the organs in the image and then proceed towards contrast enhancement in those areas. To achieve this, we use a correlation based algorithm described in the next section.
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	= Future Work =		= Future Work =
	*The algorithms developed in this project can be further tested with more data sets. In future tests, we would take extreme care to ensure that the photon illumination of our data sets is uniform, as we believe that the reflectance estimation is compromised by non-uniform illumination of the organ.		*The algorithms developed in this project can be further tested with more data sets. In future tests, we would take extreme care to ensure that the photon illumination of our data sets is uniform, as we believe that the reflectance estimation is compromised by non-uniform illumination of the organ.
	*Imaging the effects of altered blood supply to organs have ~~previously~~ been studied~~. These~~ algorithms can be applied to pre and post-ischemic organ tissues to ~~improve early detection of~~ ischemia.		*A very interesting application of this work can be to improve early detection of ischemia. Imaging the effects of altered blood supply to organs have been studied previously, and these algorithms can be applied to pre and post-ischemic organ tissues to render them with high contrast, thereby detecting ischemia.
	*The algorithms developed in this project could be further implemented at the hardware level to apply real-time image processing. This would allow physicians to simply turn these enhancement features on to help differentiate between similar looking organs.		*The algorithms developed in this project could be further implemented at the hardware level to apply real-time image processing. This would allow physicians to simply turn these enhancement features on to help differentiate between similar looking organs.