Threshold Segmentation

#HeaderInfo

#type=ROITool

#name=Threshold Segmentation

#version=0.0.1

#author=adminmblackledge

#EndHeaderInfo

import matplotlib.pyplot as pl

from matplotlib.widgets import Button, Slider

import numpy as np

class threshold_segmentation(object):

    def __init__(self):

        self.threshold = 0

        self.viewer_controller = None

    def _update(self, thresh_value):

        # Update the displayed matplotlib window

        cur_image = self.viewer_controller.curDCM().image

        self.ax_image.cla()

        self.ax_image.set_xticks([]), self.ax_image.set_yticks([]) # Remove the labels on the axes

        self.ax_image.imshow(cur_image.T, cmap = 'gray', interpolation = 'None')

        thresh_min = self.slider_min.val

        thresh_max = self.slider_max.val

        mask = np.logical_and(cur_image < thresh_max, cur_image > thresh_min)

        self.ax_image.contour(mask.T, [0.5], colors = ['r']) 

    # Widget events

    def _compute_pushed(self, evt):

        # Compute ROIs for all images in the viewer controller

        thresh_min = self.slider_min.val

        thresh_max = self.slider_max.val

        curIdx = self.viewer_controller.movieIdx # The current movie index

        pix_list = self.viewer_controller.pixList(curIdx)

        for i in range(len(pix_list)):

            pix = pix_list[i]

            mask = np.logical_and(pix.image < thresh_max, pix.image > thresh_min)

            roi = osirix.ROI(itype = 'tPlain', buffer = mask, DCMPix = pix)

            roi.name = "Threshold Segmentation"

            roi.color = (255, 0, 255) # Purple Color

            roi.opacity = 0.5

            self.viewer_controller.setROI(roi, position = i, movieIdx = curIdx)

        pl.close(self.fig)

        stopRuntimeLoop()

    def _cancel_pushed(self, evt):

        pl.close(self.fig)

        stopRuntimeLoop() # Required for installed pyOsiriX plugins

    def _close_event(self, evt):

        # Get here when user closes the window 

        stopRuntimeLoop() # Required for installed pyOsiriX plugins

    def display(self):

        # The current viewer controller

        self.viewer_controller = osirix.frontmostViewer()

        # Get the currently displayed image - to extract image statistics

        cur_image = self.viewer_controller.curDCM().image

        im_min = np.min(cur_image)

        im_max = np.max(cur_image)

        # Begin display of the matploblib window

        self.fig = pl.figure(figsize = (5, 5))

        # To display the current image

        self.ax_image = self.fig.add_axes([0.15, 0.35, 0.7, 0.55])

        self.ax_image.set_xticks([]), self.ax_image.set_yticks([])

        # Min/max threshold sliders

        ax_slider = self.fig.add_axes([0.25, 0.25, 0.6, 0.05])

        self.slider_min = Slider(ax_slider, label="Min Thresh.",\

                               valmin=im_min, valmax=im_max, valinit=im_min, valfmt="%.2f")

        self.slider_min.on_changed(self._update)

        ax_slider = self.fig.add_axes([0.25, 0.15, 0.6, 0.05])

        self.slider_max = Slider(ax_slider, label="Max Thresh.",\

                               valmin=im_min, valmax=im_max, valinit=im_max, valfmt="%.2f")

        self.slider_max.on_changed(self._update)

        # Compute/Cancel buttons

        ax_button = self.fig.add_axes([0.2, 0.05, 0.25, 0.05])

        self.button_compute =   Button(ax_button, label = 'Compute')

        self.button_compute.on_clicked(self._compute_pushed)

        ax_button = self.fig.add_axes([0.55, 0.05, 0.25, 0.05])

        self.button_cancel =   Button(ax_button, label = 'Cancel')

        self.button_cancel.on_clicked(self._cancel_pushed)

        self.fig.canvas.mpl_connect('close_event', self._close_event)

        # Update the matplotlib window display

        self._update(None)

        pl.show()

startRuntimeLoop() # Required for installed pyOsiriX plugins to keep the event loop going.

threshold = threshold_segmentation()

threshold.display()