--- a
+++ b/README.md
@@ -0,0 +1,23 @@
+<div class="sc-cmRAlD dkqmWS"><div class="sc-UEtKG dGqiYy sc-flttKd cguEtd"><div class="sc-fqwslf gsqkEc"><div class="sc-cBQMlg kAHhUk"><h2 class="sc-dcKlJK sc-cVttbi gqEuPW ksnHgj">About Dataset</h2></div></div></div><div class="sc-jgvlka jFuPjz"><div class="sc-gzqKSP ktvwwo"><div style="min-height: 80px;"><div class="sc-etVRix jqYJaa sc-bMmLMY ZURWJ"><p>This dataset contains <strong>segmented images</strong> of white blood cells derived from the <strong>Peripheral Blood Cell Dataset</strong> available on Kaggle. The original dataset consists of 17,092 images of individual normal cells, acquired using the analyzer CellaVision DM96 in the Core Laboratory at the Hospital Clinic of Barcelona. These images were organized into eight groups: <em>neutrophils</em>, <em>eosinophils</em>, <em>basophils</em>, <em>lymphocytes</em>, <em>monocytes</em>, <em>immature granulocytes</em>, <em>erythroblasts</em>, and <em>platelets</em>.</p>
+<p>In this dataset, we provide 195 images per class, each with a corresponding binary mask created using <strong>OpenCV</strong> techniques, including <strong>GrabCut</strong> and <strong>morphological</strong> operations. This dataset is suitable for training segmentation models like <strong>U-Net</strong>, <strong>Mask R-CNN</strong>, and other advanced image segmentation techniques.</p>
+<p><strong>File Structure:</strong></p>
+<p><strong>original_images/</strong><br>
+<strong>Description:</strong> Original images of white blood cells from the Peripheral Blood Cell Dataset, categorized into eight classes.<br>
+<strong>File Count:</strong> 1,560 images (195 per class)<br>
+<strong>File Format:</strong> JPG<br>
+<strong>Naming Convention:</strong> class_name_id.jpg</p>
+<p><strong>binary_masks/</strong><br>
+<strong>Description:</strong> Binary masks for the white blood cell images, created using GrabCut and morphological techniques.<br>
+<strong>File Count:</strong> 1,560 masks (195 per class)<br>
+<strong>File Format:</strong> JPG<br>
+<strong>Naming Convention:</strong> class_name_id.jpg</p>
+<h4>Applications</h4>
+<ul>
+<li><strong>Training Segmentation Models</strong>: Use this dataset to train models for segmenting white blood cells.</li>
+<li><strong>Educational Purposes</strong>: A valuable resource for learning and practicing advanced segmentation techniques.</li>
+<li><strong>Benchmarking</strong>: Compare different segmentation algorithms and models.</li>
+</ul>
+<h4>Methodology</h4>
+<p>The segmentation masks were generated using the GrabCut technique, followed by morphological operations to refine the masks. This approach ensures accurate and high-quality segmentation, making the dataset suitable for various image segmentation tasks.</p>
+<h4>License</h4>
+<p>This dataset is licensed under the MIT License. Please refer to the LICENSE file for further information.</p></div></div></div>
\ No newline at end of file