Abstract
Ultrasound (US) is one of the best imaging modalities on thyroid identification. The suspicious thyroid is indicated in the existence of palpable nodules whose solid or cystic composition. Solid nodules have high possibility to be malignant than cystic. An effort to detect and classify the internal content of thyroid nodule has become challenge problem in radiology area. Operator dependence of ultrasound imaging makes it complicated due to missing interpretation among radiologists. Objective Computer Aided Diagnosis (CAD) was designed to solve it which works on texture analysis of histogram statistic, gray level co-occurrence matrice (GLCM) and gray level run length matrices (GLRLM). The fine-needle aspiration cytology (FNAC) is not needed because the textural pattern is significantly different between solid and cystic nodules. Multi-layer perceptron (MLP) was adopted to do classification process for 72 US thyroid images yield an accuracy of 90.28%, the sensitivity of 87.80%, specificity of 93.55% and precision of 94.74%.
Highlights
Thyroid nodules are solid or liquid lumps in the thyroid gland
This study aims to classify thyroid nodules into solid and cystic as core parameters on thyroid cancer
Features of extraction were carried out to obtain measurable textural values on varies images of thyroid nodules based on the statistical histogram, Gray Level Co-occurrence Matrices (GLCM), and gray level run length matrices (GLRLM)
Summary
Thyroid nodules are solid or liquid lumps in the thyroid gland. The incidences of nodule are the common problem in society and increasing with age [1]. About 4-7% of adults possess palpable nodules, biopsy, and ultrasound imaging detects these well [2]. Thyroid nodules are more common in elderly patients, women and people with iodine deficiency [3]. More 90% of thyroid nodules are benign and do not require special treat ment. 5% nodules are ma lignant, require early detection and comprehensive treatment [4]
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