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Automatic texture and intensity based ovarian classification.

Kiruthika V1, Sathiya S2, Ramya M M3

  • 1a Department of Electronics & Instrumentation Engineering , Hindustan Institute of Technology and Science , Chennai , India.

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|April 2, 2019
PubMed
Summary

This study introduces a computer-assisted method for detecting ovarian follicles and cysts, improving accuracy in infertility treatment. The novel texture and intensity-based ovarian classification (TIOC) system aids medical experts in diagnosis.

Keywords:
Discrete wavelet transformk-means clusteringovarian classificationovarian follicle detectiontexture features

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Area of Science:

  • Medical Imaging
  • Biomedical Engineering
  • Gynecology

Background:

  • Ovarian follicle/cyst detection is crucial for female infertility treatment.
  • Ultrasound imaging is used, but human interpretation faces challenges due to variations and noise.
  • Accurate follicle recognition is difficult due to non-homogenous tissue and speckle noise.

Purpose of the Study:

  • To develop a computer-assisted system for ovarian follicle and cyst recognition and classification.
  • To enhance diagnostic accuracy and reduce misinterpretation in infertility management.
  • To provide a decision support system for medical experts.

Main Methods:

  • Utilized discrete wavelet transform (dwt) for image despeckling.
  • Employed texture and intensity-based segmentation for automatic follicle recognition.
  • Performed ovarian classification based on ovarian morphology using the TIOC method.

Main Results:

  • The TIOC method demonstrated high efficiency in ovarian classification.
  • Performance was validated using sensitivity, specificity, accuracy, precision, and ROC curves.
  • Results showed improved accuracy compared to control and existing methods.

Conclusions:

  • The proposed computer-assisted method offers a reliable decision support system for diagnosing ovarian conditions.
  • The TIOC approach effectively overcomes challenges in follicle recognition and classification.
  • This technology has the potential to improve infertility treatment outcomes.