Here, we show that the impact of brain motion on images collected through in vivo two-photon microscopy can be substantially reduced by synchronizing image acquisition to the cardiac cycle.įor craniotomies, mice were anaesthetized by i.p. Nevertheless, these post hoc in-frame correction approaches rely on statistical assumptions that are difficult to validate, and data lost during image acquisition cannot be recovered. The impact of these frequent movements can be reduced using line-by-line correction algorithms based on the Lucas–Kanade framework or hidden Markov models ( Dombeck et al. However, it is more difficult to compensate motion artifacts that occur during the acquisition of individual image frames (in-frame motion artifacts).
Post hoc application of whole-frame registration algorithms based on cross-correlation ( Rosenfeld & Kak, 1982) is effective at reducing the impact of both translational movements of the brain and thermal drift arising from instrumentation. 2010), leaving the challenge of brain motion unresolved. Although it is possible to reduce brain movement in the open skull configuration by pressing a glass coverslip or other transparent material against the surface of the brain, such manipulations are not possible when imaging is combined with drug application, or when imaging is performed using less traumatic thinned skull preparations ( Dombeck et al. In vivo imaging of fluorescent structures in the brain requires removal or thinning of the overlying skull. The complex motions produced by these vital functions reduce image stability, compromise the resolving power of the microscope, and limit our ability to study both physiological responses and pathological changes in the intact CNS. However, rapid movements of the brain due to beating of the heart and breathing present an inherent challenge for in vivo studies. To accurately follow cellular dynamics, such as Ca 2+ changes in subcellular compartments, the growth of dendritic spines, and organelle movements, it is essential to maximize spatial resolution.
Once a license plate has been accurately identified, information about the vehicle can be obtained from various database and that`s why I used Matlab R2010a Simulation to stimulate my project.In vivo two-photon imaging has become an indispensable approach for monitoring changes in the morphology and physiological activity of cells in the central nervous system ( Kerr & Denk, 2008). The function of a neural network is to produce an output pattern when presented with an input pattern.īecause Neural Network is used to recognize every single character of car plate numbers. In other words, neural network functions in a way similar to the human brain. What is Neural Network ? Neural Network is a network of interconnected neurons, inspired from the studies of the biological nervous system. Began with title page, declaration page, approval page, acknowledgement page, abstract, table of contents, chapter 1 for introduction and chapter 2 for literature review.īefore that, I would like to tell here that my project is using Neural Network. Get the illustrates an edge detection process.įor the second week of this semester, I started with drafting my technical paper and also my FYP report. If(density > LLimit_Density) & (density LLimit_Area) & (area LLimit_HWratio) & (HWratio LLimit_TotalArea) & (TotalArea = LLimit_NumOfCharacter) & (cc.NumObjects <= HLimit_NumOfCharacter) HWratio = (stats(cnt).BoundingBox(4)/stats(cnt).BoundingBox(3))ĭensity = stats(cnt).Area / (stats(cnt).BoundingBox(3)*stats(cnt).BoundingBox(4)) Rectangle('Position',stats(cnt).BoundingBox,'EdgeColor','red','LineWidth',2) Stats = regionprops(BW3, 'ConvexHull', 'Area', 'BoundingBox') įigure(fig_BW_disp) imshow(BW_disp) hold on Tf = īW3 = imtransform(BW2 & ProcessImgSegment, tform) Len = norm(lines(cnt).point1 - lines(cnt).point2) Lines = houghlines(BW2,T,R,P,'FillGap',5,'MinLength',7) % Find the skew angle by using hough transform Stats = regionprops(BW1, 'Area', 'FilledImage') ProcessImgSegment = BW_ProcessImg(y1:y2, x1:x2) Rectangle('Position', 'EdgeColor', 'Red', 'LineWidth',2) H = Filtered_HProSegment(i,2) - Filtered_HProSegment(i,1) Įnd % for j=1:size(VProSegment.Profile(j,1) If(density > LLimit_Density) & (density LLimit_Area) & (area LLimit_HWratio) & (HWratio LLimit_GradientMagnitude) & (GradientMagnitude LLimit_TotalArea) & (TotalArea = LLimit_NumOfCharacter) & (cc.NumObjects RatioBetweenSpaceDistAndAvrDist). Y2 = ceil(stats(cnt).BoundingBox(2) + stats(cnt).BoundingBox(4)) X2 = ceil(stats(cnt).BoundingBox(1) + stats(cnt).BoundingBox(3)) HWratio = (stats(cnt).BoundingBox(4)/stats(cnt).BoundingBox(3)) ĭensity = stats(cnt).Area / (stats(cnt).BoundingBox(3)*stats(cnt).BoundingBox(4))