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  • 1UFU - FACOM, av. João Neves de Ávila 2121, Bl.B, Uberlândia-MG 38400-902, Brazil; UFABC - CMCC, av. dos Estados 5001, Bl.B, St. André-SP 09210-580, Brazil.

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Summary

This study introduces a novel computer-aided diagnosis system for classifying non-Hodgkin lymphomas. The approach effectively distinguishes between lymphoma subtypes using non-morphological features from cell nuclei images, achieving high accuracy.

Keywords:
Histological imageLymphomaMorphological and non-morphological featuresPolynomialSVM

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Area of Science:

  • Medical image analysis
  • Computational pathology
  • Hematologic oncology

Background:

  • Non-Hodgkin lymphomas, including mantle cell lymphoma, follicular lymphoma, and chronic lymphocytic leukemia, present diagnostic challenges due to diverse clinical and histopathological features.
  • Accurate diagnosis is crucial for effective treatment and prognosis.
  • Computer-aided diagnosis (CAD) systems utilizing computational vision and image processing offer potential for improving lymphoma detection and classification.

Purpose of the Study:

  • To develop and evaluate a new approach for classifying lymphoma subtypes based on morphological and non-morphological features extracted from cell nuclei in histological images.
  • To enhance image processing techniques for accurate feature extraction and selection.
  • To assess the performance of various machine learning classifiers for automated lymphoma diagnosis.

Main Methods:

  • Image enhancement using contrast limited adaptive histogram equalization and 2D order-statistics filtering.
  • Nuclei segmentation using global thresholding, flood-fill, and watershed techniques.
  • Extraction of morphological features (area, perimeter, etc.) and non-morphological features.
  • Feature selection using ANOVA, Ansari-Bradley, and Wilcoxon rank sum tests.
  • Classification using polynomial, support vector machine, random forest, and decision tree models.

Main Results:

  • Non-morphological features demonstrated superior performance, achieving Area Under the Curve (AUC) and Accuracy (AC) values between 95% and 100%.
  • The proposed automated protocol showed high efficacy in distinguishing between the studied lymphoma groups.
  • The image processing and feature extraction pipeline effectively handled noise and identified relevant regions of interest.

Conclusions:

  • The developed computational approach provides a robust and automated method for diagnosing lymphoma histological tissue.
  • The high accuracy achieved, particularly with non-morphological features, highlights its potential to aid specialists in clinical practice.
  • This system offers a valuable tool for improving the efficiency and reliability of lymphoma diagnosis.