convex hull-based matlab program lobefinder  (MathWorks Inc)

Journal: Plant Physiology

Article Title: LobeFinder: A Convex Hull-Based Method for Quantitative Boundary Analyses of Lobed Plant Cells ^{1 [OPEN]}

doi: 10.1104/pp.15.00972

Figure Lengend Snippet: Overview of the LobeFinder logic and work flow. A, Cell perimeter positions are manually segmented from raw images, scaled, and resampled. Bar = 20 µm. B, A convex hull, defined as the minimal polygon that encloses the entire given cell perimeter, is computed (step 1), then the perimeter is scanned for missed lobe points (the extrema between segments 4 and 5) using the PeakFinder algorithm within MatLab (step 2). The optimized values for thresholds (δTH and λTH) for rule-based lobe geometry and spacing are used to identify putative lobe points (step 3); then, groups of lobe points are merged, and the final set of predicted lobe positions is extracted (step 4).

Article Snippet: In this article, we describe a highly useful convex hull-based MatLab program termed LobeFinder that operates on cell perimeter coordinates extracted from images of pavement cells and returns an array of useful cell shape data, including a value for lobe number and a map of their positions.

Techniques:

Journal: Plant Physiology

Article Title: LobeFinder: A Convex Hull-Based Method for Quantitative Boundary Analyses of Lobed Plant Cells ^{1 [OPEN]}

doi: 10.1104/pp.15.00972

Figure Lengend Snippet: Evaluation of LobeFinder accuracy using a calibration data set and parameter optimization. A, Example of a raw image containing five cotyledon pavement cells in the calibration data set. Bar = 20 µm. B, Outlines of extracted cells showing the cell boundary and the unrefined convex hull. C and D, Example output of LobeFinder for two cells in which the correctly identified (green squares) and missed (red arrow) lobes are marked. E, Comparison of the skeletonize method with manually curated results. The light blue circles are the median values from manual lobe identification results for each cell, with individual independent values in small dark blue dots, and red boxes are lobe numbers predicted by the skeletonize method. The dark blue bars plot the absolute value of the differences between the lobe number count from the skeletonize method and the median value from the manual results. F, Comparison of the LobeFinder method with manually curated results. The symbols and bars are as described in E, but here, the red boxes are the lobe numbers predicted by LobeFinder. The dark blue bars are the absolute value differences between the lobe number count from LobeFinder and the median value from manual results. G, Comparison of the percentage errors of the LobeFinder, skeletonize, and manual scoring methods that were calculated using the median lobe number as the correct value for each cell.

Article Snippet: In this article, we describe a highly useful convex hull-based MatLab program termed LobeFinder that operates on cell perimeter coordinates extracted from images of pavement cells and returns an array of useful cell shape data, including a value for lobe number and a map of their positions.

Techniques: Comparison

Journal: Plant Physiology

Article Title: LobeFinder: A Convex Hull-Based Method for Quantitative Boundary Analyses of Lobed Plant Cells ^{1 [OPEN]}

doi: 10.1104/pp.15.00972

Figure Lengend Snippet: LobeFinder can be used to detect new lobes and quantify growth patterns in time-lapse images. A to C, Examples of raw images of pavement cells with manually segmented cell shapes at three different intervals of cotyledon development. A, Pavement cell at 38 (left) and 55 (right) HAG. B, Pavement cell at 48 (left) and 120 (right) HAG. C, Pavement cell at 72 (left) and 120 (right) HAG. The blue boxes indicate the detection of new lobes and their location in the images and on the DTRH plots. D to F, DTRH plots for pavement cells that were rescaled to their original size. The x axes of these plots are the scaled distance along the convex hull perimeter at the two different time points to enable visual comparisons of similar relative positions along the cell boundary at the two time points. The blue line is the DTRH at the initial time point, and the dotted green line is the DTRH at the final time point. The time points in D to F correspond to those of A to C, respectively, and are shown in the legend for each plot. The blue dots and red boxes on the x axis identify lobe locations in the initial and final time points, respectively. Bars = 20 µm.

Article Snippet: In this article, we describe a highly useful convex hull-based MatLab program termed LobeFinder that operates on cell perimeter coordinates extracted from images of pavement cells and returns an array of useful cell shape data, including a value for lobe number and a map of their positions.

Techniques:

Journal: Plant Physiology

Article Title: LobeFinder: A Convex Hull-Based Method for Quantitative Boundary Analyses of Lobed Plant Cells ^{1 [OPEN]}

doi: 10.1104/pp.15.00972

Figure Lengend Snippet: Lobe number quantification for cotyledon pavement cells at different developmental stages using LobeFinder For 38 to 55 HAG , n = 10 cells; for 48 to 120 HAG , n = 12 cells; and for 72 to 120 HAG , n = 12 cells.

Article Snippet: In this article, we describe a highly useful convex hull-based MatLab program termed LobeFinder that operates on cell perimeter coordinates extracted from images of pavement cells and returns an array of useful cell shape data, including a value for lobe number and a map of their positions.

Techniques: