Matlab肺结节分割(肺结节提取)源程序，也有GUI人机界面版本。使用传统图像分割方法，非深度学习方法。使用LIDC-IDRI数据集

Matlab肺结节分割(肺结节提取)源程序也有GUI人机界面版本。使用传统图像分割方法非深度学习方法。使用LIDC-IDRI数据集。工作如下1、读取图像。读取原始dicom格式的CT图像并显示绘制灰度直方图2、图像增强。对图像进行图像增强包括Gamma矫正、直方图均衡化、中值滤波、边缘锐化3、肺质分割。基于阈值分割从原CT图像中分割出肺质4、肺结节分割。肺质分割后进行特征提取计算灰度特征、形态学特征来分割出肺结节5、可视化标注文件。读取医生的xml标注文件可视化出医生的标注结果6、计算IOU、DICE、PRE三个参数评价分割效果好坏。7、做成GUI人机界面。注需要2022b及以上版本。因为参数是根据这两张图片设置的所以只可分割压缩包里这两张图像替换成其他的图像效果不好。基于MATLAB的传统图像分割方法实现肺结节分割的完整项目包括读取DICOM图像、图像增强、肺质分割、肺结节分割、可视化标注文件以及计算评价指标。此外还包括一个GUI人机界面版本。项目结构深色版本PulmonaryNoduleSegmentation/├── data/│ ├── LIDC-IDRI-0001/│ │ ├── 1.3.6.1.4.1.14519.5.2.1.6279.6001.298806137288633453246975630178.dcm│ │ └── 1.3.6.1.4.1.14519.5.2.1.6279.6001.298806137288633453246975630179.dcm│ └── LIDC-IDRI-0002/│ ├── 1.3.6.1.4.1.14519.5.2.1.6279.6001.490157381160200744294022685271.dcm│ └── 1.3.6.1.4.1.14519.5.2.1.6279.6001.490157381160200744294022685272.dcm├── labels/│ ├── LIDC-IDRI-0001.xml│ └── LIDC-IDRI-0002.xml├── src/│ ├── main.m│ ├── gui/│ │ ├── PulmonaryNoduleSegmentation.fig│ │ └── PulmonaryNoduleSegmentation.m│ ├── image_processing/│ │ ├── read_dicom.m│ │ ├── enhance_image.m│ │ ├── segment_lung.m│ │ ├── segment_nodule.m│ │ ├── visualize_annotations.m│ │ └── evaluate_segmentation.m│ └── utils/│ ├── read_xml.m│ └── calculate_metrics.m└── README.md读取图像src/image_processing/read_dicom.mmatlab深色版本function [img, info] read_dicom(file_path)% 读取DICOM图像info dicominfo(file_path);img dicomread(info);end图像增强src/image_processing/enhance_image.mmatlab深色版本function img_enhanced enhance_image(img)% Gamma矫正gamma 1.5;img_gamma imadjust(img, [], [], gamma);% 直方图均衡化img_histeq histeq(img_gamma);% 中值滤波img_median medfilt2(img_histeq, [3 3]);% 边缘锐化img_sharpen imsharpen(img_median, ‘Amount’, 1.5);img_enhanced img_sharpen;end肺质分割src/image_processing/segment_lung.mmatlab深色版本function lung_mask segment_lung(img)% 阈值分割threshold graythresh(img);lung_mask imbinarize(img, threshold);% 形态学操作se strel(‘disk’, 5);lung_mask imopen(lung_mask, se);lung_mask imfill(lung_mask, ‘holes’);end肺结节分割src/image_processing/segment_nodule.mmatlab深色版本function nodule_mask segment_nodule(img, lung_mask)% 使用肺质掩膜img_lung img .* uint8(lung_mask);% 计算灰度特征threshold graythresh(img_lung);nodule_mask imbinarize(img_lung, threshold);% 形态学操作se strel(‘disk’, 3);nodule_mask imopen(nodule_mask, se);nodule_mask imfill(nodule_mask, ‘holes’);end可视化标注文件src/image_processing/visualize_annotations.mmatlab深色版本function visualize_annotations(img, file_path)% 读取XML标注文件annotations read_xml(file_path);% 可视化标注figure;imshow(img, []);hold on;for i 1:length(annotations)annotation annotations(i);rectangle(‘Position’, annotation, ‘EdgeColor’, ‘r’, ‘LineWidth’, 2);endhold off;end计算评价指标src/utils/calculate_metrics.mmatlab深色版本function [iou, dice, precision] calculate_metrics(gt_mask, pred_mask)% 计算交并比IoUintersection sum(sum(gt_mask pred_mask));union sum(sum(gt_mask | pred_mask));iou intersection / union;% 计算Dice系数dice 2 * intersection / (sum(sum(gt_mask)) sum(sum(pred_mask)));% 计算精确率Precisiontrue_positives sum(sum(gt_mask pred_mask));false_positives sum(sum(~gt_mask pred_mask));precision true_positives / (true_positives false_positives);endGUI人机界面src/gui/PulmonaryNoduleSegmentation.fig使用MATLAB的GUIDE工具创建一个简单的GUI界面包含按钮和图像显示区域。src/gui/PulmonaryNoduleSegmentation.mmatlab深色版本function varargout PulmonaryNoduleSegmentation(varargin)% PulmonaryNoduleSegmentation MATLAB code for PulmonaryNoduleSegmentation.fig% PULMONARYNODULESEGMENTATION, by itself, creates a new PULMONARYNODULESEGMENTATION or raises the existing% singleton*.%% H PULMONARYNODULESEGMENTATION returns the handle to a new PULMONARYNODULESEGMENTATION or the handle to% the existing singleton*.%% PULMONARYNODULESEGMENTATION(‘CALLBACK’,hObject,eventData,handles,…) calls the local% function named CALLBACK in PULMONARYNODULESEGMENTATION.M with the given input arguments.%% PULMONARYNODULESEGMENTATION(‘Property’,‘Value’,…) creates a new PULMONARYNODULESEGMENTATION or raises the% existing singleton*. Starting from the left, property value pairs are% applied to the GUI before PulmonaryNoduleSegmentation_OpeningFcn gets called. An% unrecognized property name or invalid value makes property application% stop. All inputs are passed to PulmonaryNoduleSegmentation_OpeningFcn via varargin.%% *See GUI Options on GUIDE’s Tools menu. Choose “GUI allows only one% instance to run (singleton)”.%% See also: GUIDE, GUIDATA, GUIHANDLES% Edit the above text to modify the response to help PulmonaryNoduleSegmentation % Last Modified by GUIDE v2.5 04-Nov-2023 12:00:00 % Begin initialization code - DO NOT EDIT gui_Singleton 1; gui_State struct(gui_Name, mfilename, ... gui_Singleton, gui_Singleton, ... gui_OpeningFcn, PulmonaryNoduleSegmentation_OpeningFcn, ... gui_OutputFcn, PulmonaryNoduleSegmentation_OutputFcn, ... gui_LayoutFcn, [] , ... gui_Callback, []); if nargin ischar(varargin{1}) gui_State.gui_Callback str2func(varargin{1}); end if nargout [varargout{1:nargout}] gui_mainfcn(gui_State, varargin{:}); else gui_mainfcn(gui_State, varargin{:}); end % End initialization code - DO NOT EDIT% — Executes just before PulmonaryNoduleSegmentation is made visible.function PulmonaryNoduleSegmentation_OpeningFcn(hObject, eventdata, handles, varargin)% This function has no output args, see OutputFcn.% hObject handle to figure% eventdata reserved - to be defined in a future version of MATLAB% handles structure with handles and user data (see GUIDATA)% varargin command line arguments to PulmonaryNoduleSegmentation (see VARARGIN)% Choose default command line output for PulmonaryNoduleSegmentation handles.output hObject; % Update handles structure guidata(hObject, handles); % UIWAIT makes PulmonaryNoduleSegmentation wait for user response (see UIRESUME) % uiwait(handles.figure1);% — Outputs from this function are returned to the command line.function varargout PulmonaryNoduleSegmentation_OutputFcn(hObject, eventdata, handles)% varargout cell array for returning output args (see VARARGOUT);% hObject handle to figure% eventdata reserved - to be defined in a future version of MATLAB% handles structure with handles and user data (see GUIDATA)% Get default command line output from handles structure varargout{1} handles.output;% — Executes on button press in pushbutton1.function pushbutton1_Callback(hObject, eventdata, handles)% hObject handle to pushbutton1 (see GCBO)% eventdata reserved - to be defined in a future version of MATLAB% handles structure with handles and user data (see GUIDATA)% 读取图像 [file_path, path] uigetfile({*.dcm}, Select DICOM Image); if isequal(file_path, 0) return; end full_file_path fullfile(path, file_path); [img, info] read_dicom(full_file_path); axes(handles.axes1); imshow(img, []); title(Original Image); % 绘制灰度直方图 figure; imhist(img); title(Gray Level Histogram); % 图像增强 img_enhanced enhance_image(img); axes(handles.axes2); imshow(img_enhanced, []); title(Enhanced Image); % 肺质分割 lung_mask segment_lung(img_enhanced); axes(handles.axes3); imshow(lung_mask, []); title(Lung Segmentation); % 肺结节分割 nodule_mask segment_nodule(img_enhanced, lung_mask); axes(handles.axes4); imshow(nodule_mask, []); title(Nodule Segmentation); % 可视化标注文件 [xml_file_path, xml_path] uigetfile({*.xml}, Select XML Annotation File); if isequal(xml_file_path, 0) return; end full_xml_file_path fullfile(xml_path, xml_file_path); visualize_annotations(img, full_xml_file_path); % 计算评价指标 gt_mask read_xml(full_xml_file_path); [iou, dice, precision] calculate_metrics(gt_mask, nodule_mask); set(handles.text1, String, sprintf(IoU: %.2f, iou)); set(handles.text2, String, sprintf(Dice: %.2f, dice)); set(handles.text3, String, sprintf(Precision: %.2f, precision));主程序src/main.mmatlab深色版本% 主程序addpath(‘src/image_processing’);addpath(‘src/utils’);addpath(‘src/gui’);% 打开GUIPulmonaryNoduleSegmentation;9. 读取XML标注文件src/utils/read_xml.mmatlab深色版本function annotations read_xml(file_path)% 读取XML标注文件doc xmlread(file_path);root doc.getDocumentElement;roi_list root.getElementsByTagName(‘ROI’);annotations {}; for i 0:roi_list.getLength-1 roi roi_list.item(i); x str2double(roi.getAttribute(x)); y str2double(roi.getAttribute(y)); width str2double(roi.getAttribute(width)); height str2double(roi.getAttribute(height)); annotations{end1} [x, y, width, height]; endend10. 运行项目将数据文件如LIDC-IDRI-0001和LIDC-IDRI-0002的DICOM图像和XML标注文件放入data和labels目录。运行main.mbash深色版本cd path/to/PulmonaryNoduleSegmentationmatlab -nodesktop -nosplash -r “run(‘src/main.m’)”11. 代码说明读取图像read_dicom.m负责读取DICOM图像并显示同时绘制灰度直方图。图像增强enhance_image.m对图像进行Gamma矫正、直方图均衡化、中值滤波和边缘锐化。肺质分割segment_lung.m基于阈值分割从原CT图像中分割出肺质。肺结节分割segment_nodule.m在肺质分割后进行特征提取计算灰度特征和形态学特征来分割出肺结节。可视化标注文件visualize_annotations.m读取医生的XML标注文件并可视化出医生的标注结果。计算评价指标calculate_metrics.m计算交并比IoU、Dice系数和精确率Precision。GUI人机界面PulmonaryNoduleSegmentation.fig和PulmonaryNoduleSegmentation.m提供了一个用户友好的界面方便用户进行图像处理和结果显示。12. 注意事项数据集项目中使用的参数是根据特定的两张图像设置的因此只适用于压缩包中的这两张图像。替换成其他图像可能效果不佳。MATLAB版本需要MATLAB 2022b及以上版本。希望这些代码和说明能帮助你完成肺结节分割项目并成功实现功能。