Medical image segmentation basics in Computer Vision - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to import the library used for image segmentation.

Computer Vision

import [1] as cv

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Atensorflow

Bcv2

Cnumpy

Dmatplotlib

Attempts:

3 left

💡 Hint

Common Mistakes

Importing unrelated libraries like numpy or matplotlib for segmentation.

Using tensorflow which is for deep learning but not direct image processing here.

We use cv2 (OpenCV) for image processing tasks including segmentation.

2fill in blank

medium

Complete the code to read a medical image from a file.

Computer Vision

image = cv.[1]('scan.png', cv.IMREAD_GRAYSCALE)

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Aimread

Bimshow

Cimwrite

Dresize

Attempts:

3 left

💡 Hint

Common Mistakes

Using imshow which displays images but does not read files.

Using imwrite which saves images instead of reading.

imread reads an image file into memory. We use grayscale mode for medical scans.

3fill in blank

hard

Fix the error in the thresholding step to segment the image.

Computer Vision

_, segmented = cv.threshold(image, [1], 255, cv.THRESH_BINARY)

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A128

B256

C-1

D0

Attempts:

3 left

💡 Hint

Common Mistakes

Using 256 which is out of range for pixel values.

Using negative or zero values which do not segment properly.

The threshold value must be between 0 and 255 for 8-bit images. 128 is a common middle value.

4fill in blank

hard

Fill both blanks to create a mask and apply it to the image.

Computer Vision

mask = segmented [1] 255
result = cv.bitwise_and(image, image, mask=[2])

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A==

Bmask

C128

D!=

Attempts:

3 left

💡 Hint

Common Mistakes

Using '!=' instead of '==' which inverts the mask.

Using incorrect mask values like 128.

The mask is created by checking where segmented equals 255. Then we apply this mask to the original image.

5fill in blank

hard

Fill all three blanks to calculate Dice coefficient for segmentation evaluation.

Computer Vision

intersection = (predicted_mask [1] true_mask).sum()
dice = (2 * intersection) / (predicted_mask.sum() [2] true_mask.sum())
print('Dice coefficient:', [3])

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A&

B+

Cdice

D|

Attempts:

3 left

💡 Hint

Common Mistakes

Using bitwise OR (|) instead of AND (&) for intersection.

Adding intersection instead of predicted and true sums in denominator.

Printing a wrong variable instead of dice.

Dice coefficient uses intersection with bitwise AND (&), sums added (+), and prints the calculated dice value.