The results of the developed edge detection technique: (a) Greyscale clinical image 1, (b) Greyscale clinical image 2, (c) Edge detection in clinical image1, and (d) Edge detection in clinical image 2.

<p>The results of the developed edge detection technique: (a) Greyscale clinical image 1, (b) Greyscale clinical image 2, (c) Edge detection in clinical image1, and (d) Edge detection in clinical image 2.</p

MFs of the input variable <i>∆P</i>_<i>j</i>.

<p>MFs of the input variable <i>∆P</i>_<i>j</i>.</p

False edge detection in a smooth clinical image of <i>270</i>x<i>290</i> pixels with <i>24</i> dB noise level.

<p>False edge detection in a smooth clinical image of <i>270</i>x<i>290</i> pixels with <i>24</i> dB noise level.</p

Parameters and terminologies of input and output fuzzy sets.

<p>^a Trapezoidal MF</p><p>Parameters and terminologies of input and output fuzzy sets.</p

Fuzzy rule base for contrast adjustment.

<p>Fuzzy rule base for contrast adjustment.</p

(a) MFs for the intensity value of input pixel (b) MFs for the intensity value of output pixel.

<p>(a) MFs for the intensity value of input pixel (b) MFs for the intensity value of output pixel.</p

Work flow of the proposed edge detection technique.

<p>Work flow of the proposed edge detection technique.</p

MF plots (a) Trapezoidal, (b) Gaussian.

<p>MF plots (a) Trapezoidal, (b) Gaussian.</p

Comparison of experimental results in noisy image: (a) Original image, (b) Noisy image, (c) Sobel edge detection (d) Prewitt edge detection, (e) LoG edge detection, (f) Robert edge detection (g) Previously developed fuzzy based edge detection technique [22], (h) Canny edge detection and (i) The developed method.

<p>All the experimentation was performed on image b.</p

Fuzzy knowledge base for the developed edge detection technique.

<p>Fuzzy knowledge base for the developed edge detection technique.</p