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<p>在<a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5480585/" rel="nofollow noreferrer">this work</a>:Montouro等人指定了一种分割OCT图像的方法,如下所示:</p>
<p><img src="https://www.ncbi.nlm.nih.gov/corecgi/tileshop/tileshop.fcgi?p=PMC3&id=198749&s=69&r=1&c=1" alt="Example"/></p>
<p>我想做一个类似的细分,但我不知道怎么做。这就是我所尝试的:</p>
<pre class="lang-py prettyprint-override"><code># load image
img = cv2.imread('OCT.jpeg')
# define colors
color1 = (255,0,0)
color2 = (255,128,128)
color3 = (0,92,0)
color4 = (128,192,255)
color5 = (0,164,255)
color6 = (122,167,141)
color7 = (0,255,0)
color8 = (0,0,255)
# build 8 color image of size 256x1 in blocks of 32
lut = np.zeros([1, 256, 3], dtype=np.uint8)
lut[:, 0:32] = color1
lut[:, 32:64] = color2
lut[:, 64:96] = color4
lut[:, 96:128] = color5
lut[:, 128:160] = color6
lut[:, 160:192] = color7
lut[:, 192:256] = color8
# apply lut
result = cv2.LUT(img, lut)
# save result
cv2.imwrite('lut.png', lut)
cv2.imwrite('OCT_colorized.png', result)
</code></pre>
<p>我得到了这个结果:</p>
<p><a href="https://i.stack.imgur.com/ebxbe.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/ebxbe.png" alt="my"/></a></p>
<p>这不是我想要的。我怎样才能重现Montuoro等人在工作中所做的事情</p>